Histopathological Image Analysis: A Review <s> I. INTRODUCTION AND MOTIVATION <s> Bayesian belief networks have a dynamic range and numeric response characteristics that make them uniquely suitable for descriptive classification schemes. Features showing considerable overlap of tolerance regions may be used, in a cumulative manner, to derive unequivocal classification decisions. The numeric response characteristics of Bayesian belief networks are analyzed, and their application as control modules in automated scene segmentation in histopathology is demonstrated. <s> BIB001 </s> Histopathological Image Analysis: A Review <s> I. INTRODUCTION AND MOTIVATION <s> AIM ::: To develop an expert system model for the diagnosis of fine needle aspiration cytology (FNAC) of the breast. ::: ::: ::: METHODS ::: Knowledge and uncertainty were represented in the form of a Bayesian belief network which permitted the combination of diagnostic evidence in a cumulative manner and provided a final probability for the possible diagnostic outcomes. The network comprised 10 cytological features (evidence nodes), each independently linked to the diagnosis (decision node) by a conditional probability matrix. The system was designed to be interactive in that the cytopathologist entered evidence into the network in the form of likelihood ratios for the outcomes at each evidence node. ::: ::: ::: RESULTS ::: The efficiency of the network was tested on a series of 40 breast FNAC specimens. The highest diagnostic probability provided by the network agreed with the cytopathologists' diagnosis in 100% of cases for the assessment of discrete, benign, and malignant aspirates. Atypical probably benign cases were given probabilities in favour of a benign diagnosis. Suspicious cases tended to have similar probabilities for both diagnostic outcomes and so, correctly, could not be assigned as benign or malignant. A closer examination of cumulative belief graphs for the diagnostic sequence of each case provided insight into the diagnostic process, and quantitative data which improved the identification of suspicious cases. ::: ::: ::: CONCLUSION ::: The further development of such a system will have three important roles in breast cytodiagnosis: (1) to aid the cytologist in making a more consistent and objective diagnosis; (2) to provide a teaching tool on breast cytological diagnosis for the non-expert; and (3) it is the first stage in the development of a system capable of automated diagnosis through the use of expert system machine vision. <s> BIB002 </s> Histopathological Image Analysis: A Review <s> I. INTRODUCTION AND MOTIVATION <s> A computerized method to automatically detect malignant masses on digital mammograms based on bilateral subtraction to identify asymmetries between left and right breast images was developed. After the digitization, in order to align left and right mammograms the breast border and nipple were automatically detected. Images were corrected to avoid differences in brightness due to the recording procedure. Left and right mammograms were subtracted and a threshold was applied to obtain a binary image with the information of suspicious areas. The suspicious regions or asymmetries were delimited by a region growing algorithm. Size and eccentricity tests were used to eliminate false-positive responses and texture features were extracted from suspicious regions to reject normal tissue regions. The scheme, tested in 70 pairs of digital mammograms, achieved a true-positive rate of 71% with an average number of 0.67 false positives per image. Computerized detection was evaluated by using free-response operating characteristic analysis (FROC). An area under the AFROC (A1) of 0.667 was obtained. Our results show that the scheme may be helpful to the radiologists by serving as a second reader in mammographic screening. The low number of false positives indicates that our scheme would not confuse the radiologist by suggesting normal regions as suspicious. <s> BIB003 </s> Histopathological Image Analysis: A Review <s> I. INTRODUCTION AND MOTIVATION <s> PURPOSE ::: To compare spatial patterns of blood vessels between invasive breast carcinomas and fibroadenomas to improve the diagnostic specificity of noninvasive vascular magnetic resonance imaging and color Doppler ultrasound. ::: ::: ::: MATERIALS AND METHODS ::: Nineteen invasive ductal carcinomas and 20 fibroadenomas from 39 patients were stained for factor VIII-related antigen. Vessels smaller than 40 microns were counted in x200 fields defined in peripheral and central areas of the tumor and in normal tissue adjacent to fibroadenomas. Significant differences in vessel density were determined with Student t tests and one-way analyses of variance. Distributions of vessels 40 microns or larger were qualitatively evaluated. ::: ::: ::: RESULTS ::: There were 9-105 vessels (mean, 31.4 vessels) smaller than 40 microns per x200 peripheral and 4-57 vessels (mean, 20.1 vessels) smaller than 40 microns per x200 central carcinoma field. There was no significant difference in vessel density between the two groups. Peripheral microvessel density was significantly higher (P < .01) than central microvessel density in 15 (79%) of the 19 carcinomas and in three (16%) of 19 fibroadenomas. (A Student t test could not be performed in one case of fibroadenoma; size permitted only one countable field according to the authors' criteria.) The 18 cases with normal tissue had 21-229 vessels (mean, 80.5 vessels) per field; in 16 (89%) of the 18 cases, these vessel counts were significantly higher (P < .01) than those in the fibroadenoma cases. Vessels 40 microns or larger were found mainly in the periphery of carcinomas and were more uniformly distributed in fibroadenomas. ::: ::: ::: CONCLUSION ::: Evaluation of the spatial distribution of vessels and the ability to resolve vessel sizes may add valuable information to the imaging-based diagnostic work-up of indeterminate solid breast lesions. <s> BIB004
T HE widespread use of computer-assisted diagnosis (CAD) can be traced back to the emergence of digital mammography in the early 1990s BIB003 . Recently, CAD has become a part of routine clinical detection of breast cancer on mammograms at many screening sites and hospitals  in the United States. In fact, CAD has become one of the major research subjects in medical imaging and diagnostic radiology. Given recent advances in high-throughput tissue bank and archiving of digitized histological studies, it is now possible to use histological tissue patterns with computer-aided image analysis to facilitate disease classification. There is also a pressing need for CAD to relieve the workload on pathologists by sieving out obviously benign areas, so that the pathologist can focus on the more difficult-to-diagnose suspicious cases. For example, approximately 80% of the 1 million prostate biopsies performed in the U.S. every year are benign; this suggests that prostate pathologists are spending 80% of their time sieving through benign tissue. Researchers in both the image analysis and pathology fields have recognized the importance of quantitative analysis of pathology images. Since most current pathology diagnosis is based on the subjective (but educated) opinion of pathologists, there is clearly a need for quantitative image-based assessment of digital pathology slides. This quantitative analysis of digital pathology is important not only from a diagnostic perspective, but also in order to understand the underlying reasons for a specific diagnosis being rendered (e.g., specific chromatin texture in the cancerous nuclei which may indicate certain genetic abnormalities). In addition, quantitative characterization of pathology imagery is important not only for clinical applications (e.g., to reduce/eliminate inter-and intra-observer variations in diagnosis) but also for research applications (e.g., to understand the biological mechanisms of the disease process). A large focus of pathological image analysis has been on the automated analysis of cytology imagery. Since cytology imagery often results from the least invasive biopsies (e.g., the cervical Pap smear), they are some of the most commonly encountered imagery for both disease screening and biopsy purposes. Additionally, the characteristics of cytology imagery, namely the presence of isolated cells and cell clusters in the images and the absence of more complicated structures such as glands make it easier to analyze these specimens compared to histopathology. For example, the segmentation of individual cells or nuclei is a relatively easier process in such imagery since most of the cells are inherently separated from each other. Histopathology slides, on the other hand, provide a more comprehensive view of disease and its effect on tissues, since the preparation process preserves the underlying tissue architecture. As such, some disease characteristics, e.g., lymphocytic infiltration of cancer, may be deduced only from a histopathology image. Additionally, the diagnosis from a histopathology image remains the "gold standard" in diagnosing considerable number of diseases including almost all types of cancer  . The additional structure in these images, while providing a wealth of information, also presents a new set of challenges from an automated image analysis perspective. It is expected that the proper leverage of this spatial information will allow for more specific characterizations of the imagery from a diagnostic perspective. The analysis of histopathology imagery has generally followed directly from techniques used to analyze cytology imagery. In particular, certain characteristics of nuclei are hallmarks of cancerous conditions. Thus, quantitative metrics for cancerous nuclei were developed to appropriately encompass the general observations of the experienced pathologist, and were tested on cytology imagery. These same metrics can also be applied to histopathological imagery, provided histological structures such as cell nuclei, glands, and lymphocytes have been adequately segmented (a complication due to the complex structure of histopathological imagery). The analysis of the spatial structure of histopathology imagery can be traced back to the works of Wiend et al. BIB004 , Bartels BIB001 and Hamilton BIB002 but has largely been overlooked perhaps due to the lack of computational resources and the relatively high cost of digital imaging equipment for pathology. However, spatial analysis of histopathology imagery has recently become the backbone of most automated histopathology image analysis techniques. Despite the progress made in this area thus far, this is still a large area of open research due to the variety of imaging methods and disease-specific characteristics.