The current study presents a quantitative approach towards visually lossless compression ratio (CR) threshold determination of JPEG2000 in digitized mammograms. This is achieved by identifying quantitative image quality metrics that reflect radiologists’ visual perception in distinguishing between original and wavelet-compressed mammographic regions of interest containing microcalcification clusters (MCs) and normal parenchyma, originating from 68 images from the Digital Database for Screening Mammography. Specifically, image quality of wavelet-compressed mammograms (CRs, 10:1, 25:1, 40:1, 70:1, 100:1) is evaluated quantitatively by means of eight image quality metrics of different computational principles and qualitatively by three radiologists employing a five-point rating scale. The accuracy of the objective metrics is investigated in terms of (1) their correlation (r) with qualitative assessment and (2) ROC analysis (Az index), employing pooled radiologists’ rating scores as ground truth. The quantitative metrics mean square error, mean absolute error, peak signal-to-noise ratio, and structural similarity demonstrated strong correlation with pooled radiologists’ ratings (r, 0.825, 0.823, −0.825, and −0.826, respectively) and the highest area under ROC curve (Az, 0.922, 0.920, 0.922, and 0.922, respectively). For each quantitative metric, the highest accuracy values of corresponding ROC curves were used to define metric cut-off values. The metrics cut-off values were subsequently used to suggest a visually lossless CR threshold, estimated to be between 25:1 and 40:1 for the dataset analyzed. Results indicate the potential of the quantitative metrics approach in predicting visually lossless CRs in case of MCs in mammography.
Image compression; Visually lossless; Compression ratio threshold; Image quality metrics; JPEG2000; Mammography; Microcalcification cluster
To evaluate the feasibility of an iPad-based documented patient briefing for Magnetic Resonance Imaging (MRI) examinations. A standard briefing sheet and questionnaire for a MRI scan was converted from paper form into an iPad application. Twenty patients, who had been referred for an MRI scan, were briefed about the examination in paper form as well as via the iPad application before performing the MRI scan. Time each patient needed for the briefing and the number of questions that came up were documented. Patients’ acceptance of the electronic briefing was assessed using a questionnaire. The mean processing time was 2.36 min (range 0.58 to 09.35 min., standard deviation ±2.05 min) for the paper-based briefing and 4.15 min (range 1.56 to 13.48 min, SD ± 2.36 min) for the app-based briefing. Concerning technical aspects, patients asked two questions during the app-based briefing; no questions arose during the paper-based briefing. Six patients preferred electronic briefing and four patients, the paper-based form. No patient preferred the electronic form with additional multimedial information. Eight participants did not mind which briefing version was used; two participants did not express their preference at all. Our experiences showed that electronic briefing using an iPad is feasible and has the potential to become a user-friendly alternative to the conventional paper-based approach. Owing to the broad range of the results, a follow-up study will seek to determine the influencing factors on processing time and other potential questions.
iPad; Electronic patient consent; Tablet PC; App; Feasibility; Apple
In clinical diagnosis of nasopharyngeal carcinoma (NPC) lesion, clinicians are often required to delineate boundaries of NPC on a number of tumor-bearing magnetic resonance images, which is a tedious and time-consuming procedure highly depending on expertise and experience of clinicians. Computer-aided tumor segmentation methods (either contour-based or region-based) are necessary to alleviate clinicians’ workload. For contour-based methods, a minimal user interaction to draw an initial contour inside or outside the tumor lesion for further curve evolution to match the tumor boundary is preferred, but parameters within most of these methods require manual adjustment, which is technically burdensome for clinicians without specific knowledge. Therefore, segmentation methods with a minimal user interaction as well as automatic parameters adjustment are often favored in clinical practice. In this paper, two region-based methods with parameters learning are introduced for NPC segmentation. Two hundred fifty-three MRI slices containing NPC lesion are utilized for evaluating the performance of the two methods, as well as being compared with other similar region-based tumor segmentation methods. Experimental results demonstrate the superiority of adopting learning in the two introduced methods. Also, they achieve comparable segmentation performance from a statistical point of view.
Nasopharyngeal carcinoma; Magnetic resonance image; Tumor segmentation; Spectral clustering; Support vector machine; Metric learning
Noise levels observed in positron emission tomography (PET) images complicate their geometric interpretation. Post-processing techniques aimed at noise reduction may be employed to overcome this problem. The detailed characteristics of the noise affecting PET images are, however, often not well known. Typically, it is assumed that overall the noise may be characterized as Gaussian. Other PET-imaging-related studies have been specifically aimed at the reduction of noise represented by a Poisson or mixed Poisson + Gaussian model. The effectiveness of any approach to noise reduction greatly depends on a proper quantification of the characteristics of the noise present. This work examines the statistical properties of noise in PET images acquired with a GEMINI PET/CT scanner. Noise measurements have been performed with a cylindrical phantom injected with 11C and well mixed to provide a uniform activity distribution. Images were acquired using standard clinical protocols and reconstructed with filtered-backprojection (FBP) and row-action maximum likelihood algorithm (RAMLA). Statistical properties of the acquired data were evaluated and compared to five noise models (Poisson, normal, negative binomial, log-normal, and gamma). Histograms of the experimental data were used to calculate cumulative distribution functions and produce maximum likelihood estimates for the parameters of the model distributions. Results obtained confirm the poor representation of both RAMLA- and FBP-reconstructed PET data by the Poisson distribution. We demonstrate that the noise in RAMLA-reconstructed PET images is very well characterized by gamma distribution followed closely by normal distribution, while FBP produces comparable conformity with both normal and gamma statistics.
Image processing; Positron emission tomography (PET); Image denoising; Nuclear medicine
A system to characterize normal liver, cirrhotic liver and hepatocellular carcinoma (HCC) evolved on cirrhotic liver is proposed in this paper. The study is performed with 56 real ultrasound images (15 normal, 16 cirrhotic and 25 HCC liver images) taken from 56 subjects. A total of 180 nonoverlapping regions of interest (ROIs), i.e. 60 from each image class, are extracted by an experienced participating radiologist. The multiresolution wavelet packet texture descriptors, i.e. mean, standard deviation and energy features, are computed from all 180 ROIs by using various compact support wavelet filters including Haar, Daubechies (db4 and db6), biorthogonal (bior3.1,bior3.3 and bior4.4), symlets (sym3 and sym5) and coiflets (coif1 and coif2). It is observed that a combined texture descriptor feature vector of length 48 consisting of 16 mean, 16 standard deviation and 16 energy features estimated from all 16 subband feature images (wavelet packets) obtained by second-level decomposition with two-dimensional wavelet packet transform by using Haar wavelet filter gives the best characterization performance of 86.6 %. Feature selection by genetic algorithm-support vector machine method increased the classification accuracy to 88.8 % with sensitivity of 90 % for detecting normal and cirrhotic cases and sensitivity of 86.6 % for HCC cases. Considering limited sensitivity of B-mode ultrasound for detecting HCCs evolved on cirrhotic liver, the sensitivity of 86.6 % for HCC lesions obtained by the proposed computer-aided diagnostic system is quite promising and suggests that the proposed system can be used in a clinical environment to support radiologists in lesion interpretation.
Texture analysis; Liver ultrasound images; Cirrhosis; Hepatocellular carcinoma; Small hepatocellular carcinoma; Large hepatocellular carcinoma; Wavelet packet transform; Multiresolution analysis; Genetic algorithm; Support vector machines; Computer-aided diagnostic system
In RIS-PACS systems, potential errors occurring during the execution of a radiologic examination can amplify the clinical risks of the patient during subsequent treatments, e.g., of oncologic patients or of those who must do additional treatments based on the initial diagnosis. In Reggio Emilia Province Diagnostic Imaging Department (REDID) we experienced different strategies to reduce clinical risks due to patient reconciliation errors. In 2010, we developed a procedure directly integrated in our RIS-PACS that uses Health Level 7 (HL7) standard messaging, which generates an overlay with the text "under investigation" on the images of the study to be corrected. All the healthcare staff is informed of the meaning of that overlay, and only the radiologist and the emergency services staff can consult these images on PACS. The elimination of image overlay and of any access limitation to PACS was triggered to confirm of the right correction made by RIS-PACS system administrator (SA). The RIS-PACS integrated tool described in this paper allows technologists and radiologists to efficiently highlight patient exam errors and to inform all the users to minimize the overall clinical risks, with a significant savings in costs. Over the years, we have observed a steady decrease in the percentage of reconciled studies. Error reconciliation requires an effective and efficient mechanism. The RIS-PACS integrated tool described in this paper enables technologists and radiologists to quickly and efficiently highlight patient exam errors and inform all the users. Next generation of RIS-PACS could be equipped with similar reconciliation tools.
RIS; PACS; Quality assurance; Patient information reconciliation
Carpal tunnel syndrome (CTS) has been reported as one of the most common peripheral neuropathies. Carpal tunnel segmentation from magnetic resonance (MR) images is important for the evaluation of CTS. To date, manual segmentation, which is time-consuming and operator dependent, remains the most common approach for the analysis of the carpal tunnel structure. Therefore, we propose a new knowledge-based method for automatic segmentation of the carpal tunnel from MR images. The proposed method first requires the segmentation of the carpal tunnel from the most proximally cross-sectional image. Three anatomical features of the carpal tunnel are detected by watershed and polygonal curve fitting algorithms to automatically initialize a deformable model as close to the carpal tunnel in the given image as possible. The model subsequently deforms toward the tunnel boundary based on image intensity information, shape bending degree, and the geometry constraints of the carpal tunnel. After the deformation process, the carpal tunnel in the most proximal image is segmented and subsequently applied to a contour propagation step to extract the tunnel contours sequentially from the remaining cross-sectional images. MR volumes from 15 subjects were included in the validation experiments. Compared with the ground truth of two experts, our method showed good agreement on tunnel segmentations by an average margin of error within 1 mm and dice similarity coefficient above 0.9.
Carpal tunnel; Knowledge-based segmentation; MR; Deformable model; Watershed; Polygonal curve
In this paper, we consider the statistical characteristics of the so-called portal images, which are acquired prior to the radiotherapy treatment, as well as the noise that present the portal imaging systems, in order to analyze whether the well-known noise and image features in other image modalities, such as natural image, can be found in the portal imaging modality. The study is carried out in the spatial image domain, in the Fourier domain, and finally in the wavelet domain. The probability density of the noise in the spatial image domain, the power spectral densities of the image and noise, and the marginal, joint, and conditional statistical distributions of the wavelet coefficients are estimated. Moreover, the statistical dependencies between noise and signal are investigated. The obtained results are compared with practical and useful references, like the characteristics of the natural image and the white noise. Finally, we discuss the implication of the results obtained in several noise reduction methods that operate in the wavelet domain.
Portal images; Noise; Portal imaging systems
Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Image mining algorithms have been predominantly used to give the physicians a more objective, fast, and accurate second opinion on the initial diagnosis made from medical images. The objective of this work is to develop an adjunct computer-aided diagnostic technique that uses 3D ultrasound images of the ovary to accurately characterize and classify benign and malignant ovarian tumors. In this algorithm, we first extract features based on the textural changes and higher-order spectra information. The significant features are then selected and used to train and evaluate the decision tree (DT) classifier. The proposed technique was validated using 1,000 benign and 1,000 malignant images, obtained from ten patients with benign and ten with malignant disease, respectively. On evaluating the classifier with tenfold stratified cross validation, the DT classifier presented a high accuracy of 97 %, sensitivity of 94.3 %, and specificity of 99.7 %. This high accuracy was achieved because of the use of the novel combination of the four features which adequately quantify the subtle changes and the nonlinearities in the pixel intensity variations. The rules output by the DT classifier are comprehensible to the end-user and, hence, allow the physicians to more confidently accept the results. The preliminary results show that the features are discriminative enough to yield good accuracy. Moreover, the proposed technique is completely automated, accurate, and can be easily written as a software application for use in any computer.
Ovarian tumor; Texture features; Higher-order spectra; Characterization; Classification; Computer-aided diagnosis
In June 2008, the Canadian Association of Radiologists published its Standards for Irreversible Compression in Digital Diagnostic Imaging within Radiology (Canadian Association of Radiologists 2012). The study suggested that at low levels of compression there was no difference in diagnostic accuracy between uncompressed JPEG and JPEG 2000. There were two exceptions; CT neurological and CT body images resulted in lower rating of image quality (Koff et al., J Digit Imaging 22(6):569–78, 2009). The slice thicknesses used in the previous study were greater than 5 mm. However, other studies (Ringl et al., Radiology 240:869–87, 2006) suggest that thin CT slices might modify image tolerance to irreversible compression. Therefore, a new clinical evaluation using CT slices less than 3 mm was initiated. We examined CT images in four body regions (chest, body, musculoskeletal, and neurological). Twenty-five radiologists from across Canada participated. Each read a total of 70 CTs in his specialty; 10 at each of seven levels of compression (uncompressed, JPEG and JPEG 2000 at low, medium, and high compression (varying by region)). Each reader diagnosed the case, rated his confidence, and compared the compressed to the uncompressed image and rated the degree of degradation. Data were analyzed for sensitivity, specificity, accuracy, confidence, and degradation at three levels and two types of compression as well as the original image. There were no overall differences in sensitivity, specificity, accuracy, or confidence. JPEG images, at all levels of compression, were rated lower in terms of perceived difference (4.16/5 vs. 4.53/5 for JPEG 2000 and 4.68/5 for uncompressed). However, the rating of perceived difference was not significantly correlated with accuracy. Analysis of individual body regions did not reveal any systematic effects of compression in any region.
The purpose of this study is to verify objectively the rate of slice omission during paging on picture archiving and communication system (PACS) viewers by recording the images shown on the computer displays of these viewers with a high-speed movie camera. This study was approved by the institutional review board. A sequential number from 1 to 250 was superimposed on each slice of a series of clinical Digital Imaging and Communication in Medicine (DICOM) data. The slices were displayed using several DICOM viewers, including in-house developed freeware and clinical PACS viewers. The freeware viewer and one of the clinical PACS viewers included functions to prevent slice dropping. The series was displayed in stack mode and paged in both automatic and manual paging modes. The display was recorded with a high-speed movie camera and played back at a slow speed to check whether slices were dropped. The paging speeds were also measured. With a paging speed faster than half the refresh rate of the display, some viewers dropped up to 52.4 % of the slices, while other well-designed viewers did not, if used with the correct settings. Slice dropping during paging was objectively confirmed using a high-speed movie camera. To prevent slice dropping, the viewer must be specially designed for the purpose and must be used with the correct settings, or the paging speed must be slower than half of the display refresh rate.
Image quality analysis; video recording; PACS implementation; PACS management; PACS system performance
To provide prospective information about quality- and satisfaction-related product features in radiology, a customer-centered approach for acquiring clinicians' requirements and their prioritizations is essential. We introduced the Kano model for the first time in radiology to obtain such information. A Kano questionnaire, consisting of pairs of questions regarding 13 clinician requirements related to computed tomography (CT), magnetic resonance imaging (MRI) access and report turnaround time (RTT), was developed and administered. Each requirement was assigned a Kano category, and its satisfaction and dissatisfaction coefficients were calculated and presented in a Kano diagram. The data were stratified based on different clinics and on staff and resident clinicians. The time interval was evaluated between the completion of an examination and the first attempt to access the report by a clinician. Consultation for modality selection and scheduling and access to CT within 24 h and RTT within 8 to 24 h were considered as must-be requirements. Access to CT within 4 h and within 8 h, access to MRI within 8 h and within 24 h, and access to RTT within 4 h were one-dimensional requirements. The extension of operation time for CT or MRI, as well as MRI access within 4 h, was considered attractive. Eight out of nine clinics considered RTT within 8 h as a must-be requirement. There were differences in responses both among different clinics and between staff and resident clinicians. Access attempts to reports by clinicians in the first 4 h after the examination completion accounted for 65 % of CTs and 49 % of MRIs.
Kano model; Consumer satisfaction; Performance measurement; Quality; Radiology workflow
Medical image registration is an important component of computer-aided diagnosis system in diagnostics, therapy planning, and guidance of surgery. Because of its low signal/noise ratio (SNR), ultrasound (US) image registration is a difficult task. In this paper, a fully automatic non-rigid image registration algorithm based on demons algorithm is proposed for registration of ultrasound images. In the proposed method, an “inertia force” derived from the local motion trend of pixels in a Moore neighborhood system is produced and integrated into optical flow equation to estimate the demons force, which is helpful to handle the speckle noise and preserve the geometric continuity of US images. In the experiment, a series of US images and several similarity measure metrics are utilized for evaluating the performance. The experimental results demonstrate that the proposed method can register ultrasound images efficiently, robust to noise, quickly and automatically.
Computer-aided diagnosis (CAD); Image registration; Ultrasound image; Inertia force
We present a novel method for the automatic segmentation of the vertebral bodies from 2D sagittal magnetic resonance (MR) images of the spine. First, a new affinity matrix is constructed by incorporating neighboring information, which local intensity is considered to depict the image and overcome the noise effectively. Second, the Gaussian kernel function is to weight chi-square distance based on the neighboring information, which the vital spatial structure of the image is introduced to improve the accuracy of the segmentation task. Third, an adaptive local scaling parameter is utilized to facilitate the image segmentation and avoid the optimal configuration of controlling parameter manually. The encouraging results on the spinal MR images demonstrate the advantage of the proposed method over other methods in terms of both efficiency and robustness.
Segmentation; Spatial neighboring information; Gaussian weight; Chi-square distance; Local scaling
Most CT dose data aggregation methods do not currently adjust dose values for patient size. This work proposes a simple heuristic for reliably computing an effective diameter of a patient from an abdominal CT image. Evaluation of this method on 106 patients scanned on Philips Brilliance 64 and Brilliance Big Bore scanners demonstrates close correspondence between computed and manually measured patient effective diameters, with a mean absolute error of 1.0 cm (error range +2.2 to −0.4 cm). This level of correspondence was also demonstrated for 60 patients on Siemens, General Electric, and Toshiba scanners. A calculated effective diameter in the middle slice of an abdominal CT study was found to be a close approximation of the mean calculated effective diameter for the study, with a mean absolute error of approximately 1.0 cm (error range +3.5 to −2.2 cm). Furthermore, the mean absolute error for an adjusted mean volume computed tomography dose index (CTDIvol) using a mid-study calculated effective diameter, versus a mean per-slice adjusted CTDIvol based on the calculated effective diameter of each slice, was 0.59 mGy (error range 1.64 to −3.12 mGy). These results are used to calculate approximate normalized dose length product values in an abdominal CT dose database of 12,506 studies.
Computed tomography; Radiation dose; Body imaging; Quality control; Image analysis
Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computer-aided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations.
Segmentation; Lung parenchyma; Chest CT; Thresholding; Morphological operations; Multilayer feed forward neural network
High-quality computed tomography (CT) exams are critical to maximizing radiologist’s interpretive ability. Exam quality in part depends on proper contrast administration. We examined injector data from consecutive abdominal and pelvic CT exams to analyze variation in contrast administration. Discrepancies between intended IV contrast dose and flow rate with the actual administered contrast dose and measured flow rate were common. In particular, delivered contrast dose discrepancies of at least 10% occurred in 13% of exams while discrepancies in flow rate of at least 10% occurred in 42% of exams. Injector logs are useful for assessing and tracking this type of variability which may confound contrast administration optimization and standardization efforts.
Computed tomography; Computer communication networks; Computer hardware; Contrast media; Diagnostic image quality
Considering that the traditional lung segmentation algorithms are not adaptive for the situations that most of the juxtapleural nodules, which are excluded as fat, and lung are not segmented perfectly. In this paper, several methods are comprehensively utilized including optimal iterative threshold, three-dimensional connectivity labeling, three-dimensional region growing for the initial segmentation of the lung parenchyma, based on improved chain code, and Bresenham algorithms to repair the lung parenchyma. The paper thus proposes a fully automatic method for lung parenchyma segmentation and repairing. Ninety-seven lung nodule thoracic computed tomography scans and 25 juxtapleural nodule scans are used to test the proposed method and compare with the most-cited rolling-ball method. Experimental results show that the algorithm can segment lung parenchyma region automatically and accurately. The sensitivity of juxtapleural nodule inclusion is 100 %, the segmentation accuracy of juxtapleural nodule regions is 98.6 %, segmentation accuracy of lung parenchyma is more than 95.2 %, and the average segmentation time is 0.67 s/frame. The algorithm can achieve good results for lung parenchyma segmentation and repairing in various cases that nodules/tumors adhere to lung wall.
Computer-aided diagnosis; Thoracic CT image; Lung parenchyma; Segmentation; Repairing; Improved chain code; Bresenham algorithms
This study aims to assess computer-aided detection (CAD) performance with full-field digital mammography (FFDM) in very small (equal to or less than 1 cm) invasive breast cancers. Sixty-eight invasive breast cancers less than or equal to 1 cm were retrospectively studied. All cases were detected with FFDM in women aged 49–69 years from our breast cancer screening program. Radiological characteristics of lesions following BI-RADS descriptors were recorded and compared with CAD sensitivity. Age, size, BI-RADS classification, breast density type, histological type of the neoplasm, and role of the CAD were also assessed. Per-study specificity and mass false-positive rate were determined by using 100 normal consecutive studies. Thirty-seven (54.4 %) masses, 17 (25 %) calcifications, 6 (8.8 %) masses with calcifications, 7 (10.3 %) architectural distortions, and 1 asymmetry (1.5 %) were found. CAD showed an overall sensitivity of 86.7 % (masses, 86.5 %; calcifications, 100 %; masses with calcifications, 100 %; and architectural distortion, 57.14 %), CAD failed to detect 9 out of 68 cases: 5 of 37 masses, 3 of 7 architectural distortions, and 1 of 1 asymmetry. Fifteen out of 37 masses were hyperdense, and all of them were detected by CAD. No association was seen among mass morphology or margins and detectability. Per-study specificity and CAD false-positive rate was 26 % and 1.76 false marks per study. In conclusion, CAD shows a high sensitivity and a low specificity. Lesion size, histology, and breast density do not influence sensitivity. Mammographic features, mass density, and thickness of the spicules in architectural distortions do influence.
Breast neoplasm; Cancer detection; Computer-assisted detection
A rising conciousness within both the medical community and in the public has been created by the current levels of radiation exposure from increased use of computed tomography. The concern has prompted the need for more data collection and analysis of hospital and imaging center exam doses. This has spurred the American College of Radiology (ACR) to develop the Dose Index Registry (DIR), which will allow participating insitutions to compare the radiation dose from their CT exams to aggregate national CT dose data based on exam type and body part. We outline the steps involved in the process of enrolling in the DIR, the technical requirements, the challenges we encountered, and our solutions to those challenges. A sample of the quaterly report released by the ACR is presented and discussed. Enrolling in the ACR dose registry is a team effort with participation from IT, a site physicist, and a site radiologist. Participation in this registry is a great starting point to initiate a QA process for monitoring CT dose if none has been established at an institution. The ACR has developed an excellent platform for gathering, analyzing, and reporting CT dose data. Even so, each insititutions will have its own unique issues in joining the project.
Computed tomography; Radiation dose; Quality assurance
The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.
Medical imaging; Retinal images; Retinal vessel segmentation; Biomedical image analysis; Image segmentation; Bit planes; Morphological processing
We propose a joint segmentation and groupwise registration method for dynamic cardiac perfusion images that uses temporal information. The nature of perfusion images makes groupwise registration especially attractive as the temporal information from the entire image sequence can be used. Registration aims to maximize the smoothness of the intensity signal while segmentation minimizes a pixel’s dissimilarity with other pixels having the same segmentation label. The cost function is optimized in an iterative fashion using B-splines. Tests on real patient datasets show that compared with two other methods, our method shows lower registration error and higher segmentation accuracy. This is attributed to the use of temporal information for groupwise registration and mutual complementary registration and segmentation information in one framework while other methods solve the two problems separately.
Groupwise registration; Segmentation; Temporal information; Cardiac; Perfusion; MRI
Surface morphology is an important indicator of malignant potential for solid-type lung nodules detected at CT, but is difficult to assess subjectively. Automated methods for morphology assessment have previously been described using a common measure of nodule shape, representative of the broad class of existing methods, termed area-to-perimeter-length ratio (APR). APR is static and thus highly susceptible to alterations by random noise and artifacts in image acquisition. We introduce and analyze the self-overlap (SO) method as a dynamic automated morphology detection scheme. SO measures the degree of change of nodule masks upon Gaussian blurring. We hypothesized that this new metric would afford equally high accuracy and superior precision than APR. Application of the two methods to a set of 119 patient lung nodules and a set of simulation nodules showed our approach to be slightly more accurate and on the order of ten times as precise, respectively. The dynamic quality of this new automated metric renders it less sensitive to image noise and artifacts than APR, and as such, SO is a potentially useful measure of cancer risk for solid-type lung nodules detected on CT.
Computer-aided diagnosis (CAD); Lung neoplasms; Computed tomography; Surface morphology
Wireless capsule endoscopy (WCE) is a novel technology aiming for investigating the diseases and abnormalities in small intestine. The major drawback of WCE examination is that it takes a long time to examine the whole WCE video. In this paper, we present a new reduction scheme for WCE video to reduce the examination time. To achieve this task, a WCE video motion model is proposed. Under this motion model, the WCE imaging motion is estimated in two stages (the coarse level and the fine level). In the coarse level, the WCE camera motion is estimated with a combination of Bee Algorithm and Mutual Information. In the fine level, the local gastrointestinal tract motion is estimated with SIFT flow. Based on the result of WCE imaging motion estimation, the reduction scheme preserves key images in WCE video with scene changes. From experimental results, we notice that the proposed motion model is suitable for the motion estimation in successive WCE images. Through the comparison with APRS and FCM-NMF scheme, our scheme can produce an acceptable reduction sequence for browsing and examination.
Wireless capsule endoscopy; Bee algorithm; SIFT flow; Motion estimation
Evolution of communication systems, especially internet-based technologies, has probably affected Radiology more than any other medical specialty. Tremendous increase in internet bandwidth has enabled a true revolution in image transmission and easy remote viewing of the static images and real-time video stream. Previous reports of real-time telesonography, such as the ones developed for emergency situations and humanitarian work, rely on high compressions of images utilized by remote sonologist to guide and supervise the unexperienced examiner. We believe that remote sonology could be also utilized in teleultrasound exam of infant hip. We tested feasibility of a low-cost teleultrasound system for infant hip and performed data analysis on the transmitted and original images. Transmission of data was accomplished with Remote Ultrasound (RU), a software package specifically designed for teleultrasound transmission through limited internet bandwidth. While image analysis of image pairs revealed statistically significant loss of information, panel evaluation failed to recognize any clinical difference between the original saved and transmitted still images.
Telemedicine; Image quality analysis; Ultrasound; Teleradiology