Morphological and architectural characteristics of primary tissue compartments, such as epithelial nuclei (EN) and
cytoplasm, provide important cues for cancer diagnosis, prognosis, and therapeutic response prediction. We propose two
feature sets for the robust quantification of these characteristics in multiplex immunofluorescence (IF) microscopy
images of prostate biopsy specimens. To enable feature extraction, EN and cytoplasm regions were first segmented from
the IF images. Then, feature sets consisting of the characteristics of the minimum spanning tree (MST) connecting the
EN and the fractal dimension (FD) of gland boundaries were obtained from the segmented compartments. We
demonstrated the utility of the proposed features in prostate cancer recurrence prediction on a multi-institution cohort of
1027 patients. Univariate analysis revealed that both FD and one of the MST features were highly effective for
predicting cancer recurrence (p ≤ 0.0001). In multivariate analysis, an MST feature was selected for a model
incorporating clinical and image features. The model achieved a concordance index (CI) of 0.73 on the validation set,
which was significantly higher than the CI of 0.69 for the standard multivariate model based solely on clinical features
currently used in clinical practice (p < 0.0001). The contributions of this work are twofold. First, it is the first
demonstration of the utility of the proposed features in morphometric analysis of IF images. Second, this is the largest
scale study of the efficacy and robustness of the proposed features in prostate cancer prognosis.
Many medical imaging techniques available today generate 4D data sets. One such technique is functional magnetic resonance imaging (fMRI) which aims to determine regions of the brain that are activated due to various cognitive and/or motor functions or sensory stimuli. These data sets often require substantial resources for storage and transmission and hence call for efficient compression algorithms. fMRI data can be seen as a time-series of 3D images of the brain. Many different strategies can be employed for compressing such data. One possibility is to treat each 2D slice independently. Alternatively, it is also possible to compress each 3D image independently. Such methods do not fully exploit the redundancy present in 4D data. In this work, methods using 4D wavelet transforms are proposed. They are compared to different 2D and 3D methods. The proposed schemes are based on JPEG2000, which is included in the DICOM standard as a transfer syntax. Methodologies to test the effects of lossy compression on the end result of fMRI analysis are introduced and used to compare different compression algorithms.
We present the results on the development of an automated system for prostate cancer diagnosis and Gleason grading. Images of representative areas of the original Hematoxylin-and-Eosin (H&E)-stained tissue retrieved from each patient, either from a tissue microarray (TMA) core or whole section, were captured and analyzed. The image sets consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively. In diagnosis, the goal is to classify a tissue image into tumor versus non-tumor classes. In Gleason grading, which characterizes tumor aggressiveness, the objective is to classify a tissue image as being from either a low- or high-grade tumor. Several feature sets were computed from the image. The feature sets considered were: (i) color channel histograms, (ii) fractal dimension features, (iii) fractal code features, (iv) wavelet features, and (v) color, shape and texture features computed using Aureon Biosciences' MAGIC system. The linear and quadratic Gaussian classifiers together with a greedy search feature selection algorithm were used. For cancer diagnosis, a classification accuracy of 94.5% was obtained on an independent test set. For Gleason grading, the achieved accuracy of classification into low- and high-grade classes of an independent test set was 77.6%.
We present a framework for optimal rate allocation to image subbands to minimize the distortion in the joint compression and classification of JPEG2000-compressed images. The distortion due to compression is defined as a weighted linear combination of the mean-square error (MSE) and the loss in the Bhattacharyya distance (BD) between the class-conditional distributions of the classes. Lossy compression with JPEG2000 is accomplished via deadzone uniform quantization of wavelet subbands. Neglecting the effect of the deadzone, expressions are derived for the distortion in the case of two classes with generalized Gaussian distributions (GGDs), based on the high-rate analysis of Poor. In this regime, the distortion function takes the form of a weighted MSE (WMSE) function, which can be minimized using reverse water-filling. We present experimental results based on synthetic data to evaluate the efficacy of the proposed rate allocation scheme. The results indicate that by varying the weight factor balancing the MSE and the Bhattacharyya distance, we can control the trade-off between these two terms in the distortion function.