Automatic detection of Tubules in Breast Histopathological Images

Histopathological examination of tissues enables pathologists to quantify the morphological features and spatial relationships of the tissue components. This process aids them in detecting and grading diseases, such as cancer. Quite often this system leads to observer variability and therefore affects patient prognosis. Hence quantitative image-analysis techniques can be used in processing the histopathology images and to perform automatic detection and grading. This paper proposes a segmentation algorithm to segment all the objects in a breast histopathology image and identify the tubules in them. The objects including the tubules and fatty regions are identified using K-means clustering. Lumen belonging to tubules is differentiated from the fatty regions by detecting the single layered nuclei surrounding them. This is done through grid analysis and level set segmentation. Identification of tubules is important because the percentage of tubular formation is one of the parameters used in breast cancer detection and grading.