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Prostatic adenocarcinoma is the most commonly occurring cancer among men in the United States, second only to skin cancer. Currently, the only definitive method to ascertain the presence of prostatic cancer is by trans-rectal ultrasound (TRUS) directed biopsy. Owing to the poor image quality of ultrasound, the accuracy of TRUS is only 20%-25%. High-resolution magnetic resonance imaging (MRI) has been shown to have a higher accuracy of prostate cancer detection compared to ultrasound. Consequently, several researchers have been exploring the use of high resolution MRI in performing prostate biopsies. Visual detection of prostate cancer, however, continues to be difficult owing to its apparent lack of shape, and the fact that several malignant and benign structures have overlapping intensity and texture characteristics. In this paper, we present a fully automated computer-aided detection (CAD) system for detecting prostatic adenocarcinoma from 4 Tesla ex vivo magnetic resonance (MR) imagery of the prostate. Alter the acquired MR images have been corrected for background inhomogeneity and nonstandardness, novel three-dimensional (3-D) texture features are extracted from the 3-D MRI scene. A Bayesian classifier then assigns each image voxel a 'likelihood' of malignancy for each feature independently. The 'likelihood' images generated in this fashion are then combined using an optimally weighted feature combination scheme. Quantitative evaluation was performed by comparing the CAD results with the manually ascertained ground truth for the tumor on the MRI. The tumor labels on the MR slices were determined manually by an expert by visually registering the MR slices with the corresponding regions on the histology slices. We evaluated our CAD system on a total of 33 two-dimensional (2-D) MR slices from five different 3-D MR prostate studies. Five slices from two different glands were used for training. Our feature combination scheme was found to outperform the individual texture features, and also other popularly used feature combination methods, including AdaBoost, ensemble averaging, and majority voting. Further, in several instances our CAD system performed better than the experts in terms of accuracy, the expert segmentations being determined solely from visual inspection of the MRI data. In addition, the intrasystem variability (changes in CAD accuracy with changes in values of system parameters) was significantly lower than the corresponding intraobserver and interobserver variability. CAD performance was found to be very similar for different training sets. Future work will focus on extending the methodology to guide high-resolution MRI-assisted in vivo prostate biopsies. Index Terms--AdaBoost, CAD, general ensemble method, image analysis, image processing, machine learning, MRI, prostate, segmentation, 3-D texture, 4 tesla.

In this paper we present a computer-aided diagnosis (CAD) system to automatically detect prostatic adenocarcinoma from high resolution digital histopathological slides. This is especially desirable considering the large number of tissue slides that are currently analyzed manually - a laborious and time-consuming task. Our methodology is novel in that texture-based classification is performed using a hierarchical classifier within a multi-scale framework. Pyramidal decomposition is used to reduce an image into its constituent scales. The cascaded image analysis across multiple scales is similar to the manner in which pathologists analyze histopathology. Nearly 600 different image texture features at multiple orientations are extracted at every pixel at each image scale. At each image scale the classifier only analyzes those image pixels that have been determined to be tumor at the preceding lower scale. Results of quantitative evaluation on 20 patient studies indicate (1) an overall accuracy of over 90% and (2) an approximate 8-fold savings in terms of computational time. Both the AdaBoost and Decision Tree classifiers were considered and in both cases tumor detection sensitivity was found to be relatively constant across different scales. Detection specificity was however found to increase at higher scales reflecting the availability of additional discriminatory information.