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This publication presents a computer method for segmenting microcalcifications in mammograms. It makes use of morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. In the second part, a watershed segmentation of microcalcifications is carried out. This study was carried out on a test set containing 200 ROIs 512×512 pixels in size, taken from mammograms from the Digital Database for Screening Mammography (DDSM), including 100 cases showing malignant lesions and 100 cases showing benign ones. The experiments carried out yielded the following average values of the measured indices: 80.5% (similarity index), 75.7% (overlap fraction), 70.8% (overlap value), and 19.8% (extra fraction). The average time of executing all steps of the methods used for a single ROI amounted to 0.83 s.
In mammography imaging, the presence of microcalcifications, i.e., small deposits of calcium in the breast, is the primary indicator of breast cancer. However, not all microcalcifications are proof of malignancy and their distribution within the breast can be used to determine whether the clusters contain benign lesions or constitute a threat indicating a malignancy. Microcalcifications presented in Figs. 1a and and2a2a and anddd are small deposits of calcium in the breast, which appear as small bright spots on mammograms. Unfortunately, the correct detection of microcalcifications in mammograms can often be very difficult. Breasts contain variable quantities of glandular, fatty, and connective tissues, and if there are a lot of glandular tissues, the mammograms are very bright, which makes small microcalcifications poorly visible . If a physician has to examine numerous series of mammograms, their visual assessment capacity is greatly reduced. Consequently, computer-aided diagnosis (CAD) is being developed to make the diagnostic process easier for the radiologists [2–7]. The standard functions of CAD systems comprise the segmentation [8–11], feature extraction [12–15], and classification [5, 16–19] to determine whether lesions are present.
Although the improvement of each of the listed functions raises the capacity of the system, the segmentation can be considered the most significant, as the precise segmentation of lesions impacts the extraction of features and the classification. Microcalcifications were segmented using several techniques, such as morphological filters [1, 20–23], machine learning [11, 24], and the wavelet transform  method using normalized Tsallis Entropy and fuzzy sets . Most recent research based on machine learning , the wavelet transform , and active contour [8, 9] demonstrate that microcalcification segmentation is highly significant and the researchers report good results of the methods proposed.
Chen et al.  analyzed the topology/connectivity of individual microcalcifications inside a cluster using multiscale morphology. In , microcalcifications were segmented using a knowledge-based approach  with the application of machine learning methods like the pixel-based boosting classifier which automatically allows the most salient features of microcalcifications to be selected. Chen et al.  report high classification accuracies (up to 96%) and also good ROC (region of convergence) results achieved.
Batchelder  proposed the 2D wavelet transform modulus maxima method (WTMM) to detect microcalcifications in mammograms. Then, fractal geometry was used to determine benign and malignant microcalcification clusters, and in particular, a “fractal zone” and “Euclidean zones” (non-fractal) were defined. The authors analyzed 118 images of 59 patients. According to their results, the probability that fractal breast lesions are malignant is between 74 and 98% and the probability that Euclidean breast lesions are benign is between 76 and 96%.
Arikidis et al.  presented multiscale active contours method (MAC) which enable single microcalcifications to be segmented. This method requires the seed contour to be initiated manually. In , rectangular ROIs 81×81 pixels in size were analyzed and experiments were carried out for the DDSM database, with the reported mean value of the area overlap measure of 0.61±0.15.
Duarte et al.  presented a geometric active contour method (GAC) for segmenting single microcalcifications. In every instance, the active contour is initiated for a single microcalcification. In , researches used 1000 rectangular ROIs taken from mammograms from the DDSM database, sized from 20×20 pixels to 41×41 pixels. Duarte et al.  report that they obtained a mean value of the area overlap measure of 0.52±0.20.
The purpose of this publication was to propose solutions for the following:
This project uses morphological image transformations [23, 26] to detect microcalcifications, and then watershed segmentation [23, 26] which makes it possible to extract the shape of microcalcifications just as in [22, 27]. In publications by Nieniewski [22, 27], user interaction is necessary to indicate the seed point of the watershed by immersion segmentation [28, 29]. In this project, the whole segmentation process is automated and does not require combining regions by maximizing average contrast, as was done in publications by Nieniewski [22, 27, 30]. This study makes use of other gradient transformations of the image undergoing watershed segmentation and fewer interim steps during the extraction of the final shape of microcalcifications. This makes it possible to execute the entire segmentation process in the mean time of 0.83 s.
The research project used 220 ROIs with the constant dimensions of 512×512 pixels, in an 8-bit format, obtained from mammograms with the original high resolution (43.5 and 50 μm/pixel, digitized using the following scanners—Howtek 960, Lumisys 200 Laser, and Howtek MultiRad850), which came from the publicly accessible DDSM database [31, 32]. Of that number, 110 ROIs contain benign lesions, and the remaining 110 ROIs show malignant cases. The images were selected by a breast radiologist with 10 years of experience and are mainly fatty breast cases from different patients. Each ROI corresponds to a different patient. It should be noted that 20 ROIs, or more exactly 10 benign and 10 malignant ones, were used to determine the necessary parameters allowing the segmentation process to be controlled. The remaining 200 ROIs were used to test the presented segmentation method, and the results obtained are presented in the experimental part of this article. Methods which can automatically mark suspicious-looking anomalies containing potential microcalcifications form a useful functionality of CAD software. Examples of their solutions can be found in literature [33, 34]. However, these methods might mark false-positive regions which contain no microcalcifications. Consequently, the correct identification of microcalcification regions by an experienced breast radiologist is indispensable in analyzing the disease. Solutions presented in this publication concern microcalcification segmentation and make use of rectangular ROIs marked by a radiologist on the source mammogram, with suspicious-looking anomalies located in their centers. This is illustrated in Fig. 1. For every case in the DDSM database, there is a radiological diagnosis available. In addition, for images with microcalcifications, a coded contour identifying the area in which microcalcifications occur called a ground truth area (GTA) is available. Each case has four images acquired in the CC and MLO projections for the left and the right breast. CC is the cranio-caudal projection showing that central and medial part of the mamma. MLO is the medio-lateral oblique projection. In the experiments, a single view was taken, namely the CC or MLO view for each patient.
The computer-aided detection and segmentation of microcalcifications from mammograms is a complex process, also because these microcalcifications are often much dispersed in the analyzed images, have low contrast, and are difficult to distinguish from their surroundings. These features may make it difficult to correctly segment them. Brief characteristics of microcalcifications taken from  are presented below:
Detecting microcalcifications allows the contrast to be increased in the image, the noise to be removed from it, and also some of the false-positive signals of microcalcifications to be removed. The results obtained are treated as a “map” on which the approximate areas in which microcalcifications occur are marked and will be used as an auxiliary image for a more precise determination of their shape. The next step in working with mammograms is to segment microcalcifications. This will be done using the watershed segmentation [22, 26, 27] to more accurately extract microcalcification shapes. Knowing the shape of microcalcification is very important as, together with their other features, it can prove tumor malignancy. The following description can be given based on the recently published work by Chen et al. :
These differences in the variability of the distribution, the size, and the number of microcalcifications in the ROIs analyzed allow radiologists to decide on the further assessment and the possible biopsy of the breast. Consequently, a correctly performed microcalcification segmentation can greatly simplify decision-taking for the doctors. Figure 2a–c shows example ROIs with benign microcalcifications. These microcalcifications are rather spread out, there are cases of a relatively larger size, and they are less numerous than malignant microcalcifications shown in Fig. 2d–f, which, in contrast, are small, numerous, and densely distributed.
To detect microcalcifications, four stages of subsequent mammogram transformations are executed (shown in Fig. 3):
In addition, the image obtained at stage 4 was subjected to “cleaning” operations, namely
The next step in working with mammograms is to extract the microcalcification shape. The methods discussed in the previous subsection were a way of determining the masks of microcalcifications, but their shape is, in the majority of cases, dependent on the morphological operations executed, such as the emax and the reconstruction . The watershed segmentation  coupled with the use of the so-called markers which prepared the image for segmenting and control this process was used to find the shape of microcalcifications. Using markers provides additional knowledge about the objects for the segmentation process and makes their extraction more efficient. It also reduces oversegmentation. A marker is defined as a cohesive area of pixels belonging to the image. An internal marker is related to the object that should be extracted, while an external marker is related to the background. Further down, the sets of internal markers and external markers will be referred to, respectively, the internal marker and the external marker, and the sum of these two sets—simply as a marker. The next steps in segmenting the shape of microcalcifications are as follows:
The accuracy of microcalcification segmentation in mammograms from the DDSM database was estimated by measuring four indices, namely the similarity index , the overlap fraction , the overlap value , and the extra fraction .
Using the four indices—SI, OF, OV, and EF—makes it possible to exhaustively compare the similarity and differences between the analyzed regions M and R and determine the overlap fraction, the underestimation, and the extra fraction. In [8, 9], only the OV index was analyzed. During research work, ROIs with the constant dimensions of 512×512 pixels were analyzed, while for mammograms from the DDSM database, there are GTA contours identifying the areas in which microcalcifications occur. Therefore, a radiologist participated in the experiments carried out and made the appropriate assessments of the detected or undetected actual and assumed microcalcification signals, namely
A radiologist’s assessment was used to calculate the mean sensitivity (4) depending on the number of false-positive signals per image (FPI).
In contrast, the sensitivity was not analyzed in [8, 9]. In [8, 9], the segmentation was performed for every microcalcification separately and in addition for patches of various sizes, but after the previous initiation of the active contour, which can, unfortunately, be a painstaking and time-consuming activity if the number of microcalcifications is large.
Table Table11 presents the values of parameters established for the morphological detection of microcalcifications using 20 pre-selected ROIs from mammograms from the DDSM database, namely 10 benign and 10 malignant cases The 20-element training set included ROIs containing microcalcifications of various shapes, sizes, numbers, and distribution as well as brightness levels. The watershed segmentation carried out after the microcalcifications are detected is automated and requires no parameters to be used. The parameters presented in Table Table11 are selected so that the SI, OF, and OV indices are the highest possible, while the EF index is as low as possible.
After the parameters necessary to control the segmentation had been established, the method of detecting and segmenting microcalcifications was tested on the remaining 200 mammograms, using segmentations done manually by a radiologist and GTA contours. The results of these segmentations are presented in Table Table2.2. Table Table22 presents calculated statistical parameters such as the maximum value (max), minimum value (min), the mean value (mean), and the standard deviation (SD) of the following calculated indices: SI, OF, OV, and EF. Figure Figure66 shows a graph of data from Table Table2.2. Table Table3,3, in turn, presents
Table Table44 shows the time measurements, in seconds, of the morphological extraction and detection method for microcalcifications (M) used for the 200 analyzed mammograms. The presented method was implemented in the Matlab R2015a environment. Time was measured for a PC with an Intel Core i7 2 GHz processor. The average time for a single ROI 512×512 pixels in size amounts to 0.83 s, and this includes all steps of the method presented in this publication.
Examples of differences in the segmentation of microcalcifications by the computer method presented in this publication and the contours manually traced by a radiologist are presented in Figs. 8 and and9.9. These are typical results obtained during the experiments carried out. In order to make microcalcification imaging easier, all examples of mammograms have been filtered according to stage 1 of the presented method and their gray levels have been inverted. In all examples from Figs. 8 and and9,9, GTA contours are superposed. Figure Figure88 shows example results for benign cases and Fig. 9 for malignant ones. The values of calculated indices are presented next to each example extracted by the watershed segmentation. Table Table55 collates the results produced by active contour methods: MAC  and GAC  with those produced in this research work.
This publication presents a computer method for detecting and segmenting microcalcifications in mammograms from the DDSM database. It uses morphological transformations and is composed of two parts. The first part detects microcalcifications morphologically, thus allowing the approximate area of their occurrence to be determined, the contrast to be improved, and noise to be reduced in the mammograms. Then, the watershed segmentation of microcalcifications is performed. In the experiments carried out for 200 ROIs taken from mammograms from the DDSM database, the measured values of the SI, OF, OV, and EF indices amounted to, respectively, 80.5, 75.7, 70.8, and 19.8%. Higher values of the SI, OF, and OV indices and a lower value of the EF index were obtained for benign cases than for malignant ones. Compared to other solutions presented in [8, 9], the process of microcalcification segmentation was automated and the computer methods used achieved at a significant speed. In the experiments completed, the average running time of the entire processing of a single ROI 512×512 in size amounted to 0.83 s. Increasing the number of cases from the DDSM database, particularly to include different types of microcalcifications according to the classification presented in , should be considered in further research. The segmentation results produced by the computer method should be evaluated by two experienced breast radiologists, and this would additionally allow the consistency of these evaluations to be compared. It should be noted that the DDSM database is not new and it will be worthwhile to add examination results produced by the newest generation of mammographs. On the other hand, the publicly accessible DDSM database is the only one containing the highest number of images together with the detailed location of lesions and their descriptions, so many researchers are willing to use it.