An integrated DWT-wiener and marker controlled watershed driven active contours for nuclear segmentation in Histopathological Images

Nuclear segmentation of histopathological images is a crucial step in computer-aided image analysis. There are complex, diverse, dense, and even overlapping nuclei in these histopathological images, leading to a challenging task of nuclear segmentation. To overcome this challenge, this paper proposes a hybrid-attention nested UNet (Han-Net), which consists of two modules: a hybrid nested U-shaped network (H-part) and a hybrid attention block (A-part). H-part combines a nested multi-depth U-shaped network and a dense network with full resolution to capture more effective features. A-part is used to explore attention information and build correlations between different pixels. With these two modules, Han-Net extracts discriminative features, which effectively segment the boundaries of not only complex and diverse nuclei but also small and dense nuclei. The comparison in a publicly available multi-organ dataset shows that the proposed model achieves the state-of-the-art performance compared to other models.

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A Novel Curvature - Norm Based Centroid Initialized and Morphologically Driven Distance Regularized Level Sets for Nuclear Segmentation in Histopathological Images

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1496 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1335-1343

Author(s):

P.M. Shivamurthy ◽

T.N. Nagabhushan ◽

Vijaya Basavaraj

Keyword(s):

Level Sets ◽

Active Contours ◽

Geometric Approach ◽

Cancer Tissue ◽

Detection Accuracy ◽

Morphological Operations ◽

Novel Approach ◽

Accuracy Measure ◽

Malignant Breast ◽

Histopathological Images

Nuclear pleomorphism is considered to be one of the most significant shape based feature adapted in grading the cancer through the pathological studies of the H&E stained tissue slides. Microscopic study of manually extracting the feature is highly laborious and misleads the pathologists during grading. Digitization of the slides has given rise to various segmentation approaches to extract the nuclei shape to assess the degree of pleomorphism. Here, a novel approach of initializing and evolving the distance regularized level sets (DRLS) for the detection and segmentation of the nuclei has been presented. In this work, two major objectives have been achieved. First, a novel geometric approach has been devised for the detection of centroids of each nuclei in the occluded region and second, a shape prior model has been presented for the extraction of gradient information through morphological operations. The multiple level set implementation of the DRLS contours are initialized using the centroids detected and driven through the gradient computed. The proposed method has been experimented over the images of benign and malignant breast cancer tissue obtained from BeakHis dataset. A quantitative analysis of the results have shown that a 97% of object detection accuracy and 78% of overlap resolution has been achieved through the proposed model. A comparative study with that of geodesic active contours have indicated an improvement in the segmentation accuracy measure of 9-10 pixel difference.

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