IIIT Hyderabad Publications
Histopathological Image Analysis for cancer detection
Author: anisha dey
Report no: IIIT/TH/2016/2
Histopathology, the study of tissues, has a major contribution in determining prognosis of diseases at the tissue level. Quantitative analysis of histopathological images is used to determine the future course of treatment. However, the analysis is a very laborious process given the complexity of structures in tissues. Pathologists require a lot of expertise and skill to perform the analysis. Hence, development of Computer Aided Diagnostic (CAD) algorithms which rely on automated image analysis, is of great interest. CAD development in histopathology is a relatively new field of research. Some of the major hurdles in this field are non - availability of public datasets, difficulty in acquiring data and ground - truth marking. This thesis aims at developing CAD tools for assisting pathologists. In our work, we have identified two problems in this field and provided solution for the same. The first is identification of stromal regions in tissue and the second is detecting mitotic candidates. Both these are ne cessary for assessing and staging cancer. For stroma identification we propose a novel block - based approach of suppressing foreground texture to learn discriminative textural features in a block. The results of the bloc k - based stage are refined to obtain t he stroma segments. We have teste d our method on 3 datasets (two public and one locally acquired) varying in size and type (breast and prostate tissue) and have obtained around 80% accuracy and 90% sensitivity. Both the qualitative as well as the quantitat ive results show an improvement over the existing methods. The existing algorithms for mitosis detection employ a traditional approach of classification post feature - extraction. A major road - block in this problem is the presence of visual clutter ( false positives , similar in appearance to mitotic candidates ). We use biological cues to design rejection stages to get rid of the false positives for mitosis detection followed by classification. The pipeline design comprising of rejection stages adapt to variability in the magnification levels of datasets. This was confirmed by determining percentage of rejection after each stage on a locally acquired dataset at different magnification settings. We further improve the performance by incorporating an eff icient classifier regenerative random forests . The algorithm was validated on MITOS dataset against the ground truth and a recall of ,0.81,precision of 0.84 and f - measure of 0.83 was obtained.
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