Interactive Computer-Aided Diagnosis of Breast Masses: Computerized Selection of Visually Similar Image Sets From a Reference Library

Abstract

Rationale and Objectives: The clinical utility of interactive computer-aided diagnosis (ICAD) systems depends on clinical relevance and visual similarity between the queried breast lesions and the ICAD-selected reference regions. The objective of this study is to develop and test a new ICAD scheme that aims improve visual similarity of ICAD-selected reference regions. Materials and Methods:A large and diverse reference library involving 3000 regions of interests was established. For each queried breast mass lesion by the observer, the ICAD scheme segments the lesion, classifies its boundary spiculation level, and computes 14 image features representing the segmented lesion and its surrounding tissue background. A conditioned k-nearest neighbor algorithm is applied to select a set of the 25 most "similar" lesions from the reference library. After computing the mutual information between the queried lesion and each of these initially selected 25 lesions, the scheme displays the six reference lesions with the highest mutual information scores. To evaluate the automated selection process of the six "visually similar" lesions to the queried lesion, we conducted a two-alternative forced-choice observer preference study using 85 queried mass lesions. Two sets of reference lesions selected by one new automated ICAD scheme and the other previously reported scheme using a subjective rating method were randomly displayed on the left and right side of the queried lesion. Nine observers were asked to decide for each of the 85 queried lesions which one of the two reference sets was "more visually similar" to the queried lesion. Results:In classification of mass boundary spiculation levels, the overall agreement rate between the automated scheme and an observer is 58.8% (Kappa = 0.31). In observer preference study, the nine observers preferred on average the reference lesion sets selected by the automated scheme as being more visually similar than the set selected by the subjective rating approach in 53.2% of the queried lesions. The results were not significantly different for the two methods (p = 0.128). Conclusion:This study suggests that using the new automated ICAD scheme, the inter-observer variability related issues can thus be avoided. Furthermore, the new scheme maintains the similar performance level as the previous scheme using the subjective rating method that can select reference sets that are significantly more visually similar (p < 0.05) than when using traditional ICAD schemes in which the mass boundary spiculation levels are not accurately detected and quantified.

An Interactive System for Computer-Aided Diagnosis of Breast Masses

“…In this article, we demonstrated a unique interactive CAD system. Although many approaches to improve performance of clinical relevance and visual similarity of our interactive CAD schemes have been reported in a number of our previous studies [28,32,33,35,[37][38][39], this article demonstrated following three innovative characteristics or unique functions that have not been reported in previous studies.…”

“…As a result, the KNN algorithm was restricted to selecting "similar" regions each with a reasonably comparable size and an overall shape. This "early" discard process does not only improve computational efficiency and also help to reduce "semantic gap" between computer vision and human vision on evaluating the similarity between the queried and the retrieved reference regions [28].…”

“…For this purpose, the content-based image retrieval (CBIR) methods previously applied in picture archiving and communication systems and medical informatics [23,24] have also been integrated into CAD schemes [25][26][27][28][29]. Unlike conventional CAD schemes that detect and cue the suspicious abnormalities based on a "global" optimization function trained using the entirely available image database, the new CAD schemes using a CBIR approach applies an adaptive approach to generate each detection and/or diagnostic result based on the selection of local approximations as the target function for each unknown query of a suspicious testing region.…”

“…Once a suspicious region is queried, CAD scheme automatically searches for and detects a pixel with the minimum intensity value around the queried point as an initial seed of a suspicious region. From the detected growth seed, CAD scheme applies a region growth algorithm that combines a multi-layer topographic region growth and an active contour approach [28] to segment the suspicious mass region. The computed boundary contour of the segmented region will be displayed on the image.…”

A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval

“…These libraries have been used to train and validate computeraided diagnosis (CAD) systems in a variety of medical domains, including breast cancer. However, the value of CAD in clinical practice is controversial, due to their "blackbox" nature and lack of reasoning ability [7], [8], [9], [10], [11], despite significant recent progress [12], [13], [14], [15], [16], [17], [18], [19], [20] both in automated detection and characterization of breast masses. An alternative approach, espoused by efforts such as ISADS [2], eschews automated diagnosis in favor of providing medical professionals with additional context about the current case that could enable them to make a more informed decision.…”