Human performance of tissue – based diagnosis is a quite complex and not really understood procedure. Naturally, image features are recognized, classified, and discriminated in combination with external, non-image data, such as age and sex of the patient. In earlier times, numerous approaches have been undertaken to identify and measure objects and to correlate the obtained features with the tissue-based diagnosis [3
]. The automated, feature – related tumor classification was the central aim. The analyzed features included size, shape, chromogen distribution, nucleoli, or more sophisticated second order statistics data [3
]. All these trials failed in so far as they did not reach the level of clinical routinely application to our knowledge. More promising was an approach to correlate tissue structures with diagnosis information based upon syntactic structure analysis [15
]. This approach revealed some clinical significance, especially in the application of "prognosis" diagnoses [5
The development of computer technology offers new perspectives in information extraction of histological images. The prerequisites to develop a successful and accurate system are the analysis of the diagnosis algorithms. The understanding of the "diagnostic procedure" has undergone significant changes too [4
]. Contemporary with the implementation of molecular pathology/genetic methods into routine tissue – based diagnosis our understanding of the diagnostic process itself has altered. Modern pathologists distinguish at least four different types of tissue based diagnoses, which are listed in table . As shown in table there is a close association between the technical procedures to be applied and the diagnostic aim. The basic difference between the classical analysis of a histological slide (for example conventional stained slide) and that obtained by application of molecular pathology techniques is based in the visualization of the contained information: The "information extraction" of conventionally stained slides has primarily to recognize "patterns" in contrast to that of molecular pathology data which usually express a "binary information": Antigens, macromolecules, abnormal genes, etc. are either present (expressed) or not.: The visualization of a potential presence of an antigen (antibody) results in a certain color (brown, red) or not, which is correspondent to a binary decision (yes, no).
In addition to the contribution of extra-image features the diagnosis process based upon conventionally stained slides can be separated into two basic procedures, namely a) object dependent, and b) texture dependent.
The object – dependent diagnosis algorithm has to I) divide the image into an object space and a background, II) search for certain objects, III) characterize the objects, and IV) derive the diagnosis – relevant information. Technically speaking, difficulties arise in the definition of the "object space", and the efficient manner to find the objects, i.e., the sampling procedure. As shown in the EAMUS system [8
], active stratified sampling is an appropriate method to identify and measure objects present in immunohistochemically stained images. However, the transformation of object related information (object features) into a "conventional diagnosis" cannot be solved in a unique manner according to all the trials that have been undertaken in the past [5
Texture dependent analysis of histological images has been undertaken by use of graph theory approaches [10
]. In principle, these algorithms define objects as vertices (nodes), use a predefined neighborhood condition (Voronoi, O'Callaghan, or limited distance relationship) to construct the edges, and the features of the vertices (usually nuclei) and their attributes [2
]. These approaches have been reported to be successful for "classic" and "prognosis" diagnosis in lung and breast cancer [2
]. Obviously, they require similar prerequisites as object dependent diagnosis algorithms, namely the segmentation of the original image into a background and into the object space.
Herein a new approach is presented, a texture based diagnosis algorithm that does not require a segmentation algorithm. The principle idea is the definition and application of a reproducible texture algorithm that is derived from the analysis of time series. This autoregressive model can successfully be applied to create artificial textures, and to reproducible identify image textures [10
The autoregressive model creates a set of artificial textures and identifies textures of histological images with known diagnosis. Similarly, textures of images with unknown diagnosis are identified too, and compared with the artificial textures, that display the best relation to the textures of images with known diagnosis. The results are promising and convincing: all included cases could be diagnosed without false positive or negative classification. The algorithm requires a minimum image size of 50 × 50 pixels only; i.e., it is useful for image compartments too.
The random selection of non – overlapping image compartments permits an accurate screening of otherwise difficult to handle large sized images (virtual slides). Thus, texture analysis is a promising tool to screen conventionally stained histological slides, and to select those slides for further detailed human analysis that contain diagnosis useful information.