10 - Neutrosophic hough transform for blood cells nuclei detection

Abstract

Automatic blood cell (BCs) detection and nuclei detection are still challenging tasks in medical microscopic imaging systems due to their embedding in the cluttered and intricate smear. Because the BCs have an ellipsoid/circular shape, a circular Hough transform (HT) shape detector was proposed based on a neutrosophic set to extract the descriptive features of BC circular shapes in microscopic images. First, the Canny edge detector was employed to detect the edges in the microscopic image to form an edge image for further use of the HT to detect the BC. Afterward, the Hough transformed image was mapped in the neutrosophic set (NS) domain followed by the k-means clustering method to detect the nuclei within the BC images. Thus, the proposed method of two phases is called Hough transform with neutrosophic k-means (HNK) nuclei detection. The experimental results established the superiority of the proposed HNK compared to the classic HT with k-means without NS on 50 BC images in terms of the JAC, dice, sensitivity, specificity, and accuracy; it achieved 87%, 93%, 95%, 99%, and 98%, respectively.