Automated Segmentation of Leukocytes using Marker-based Watershed Algorithm from Blood Smear Images

Abstract

The aim of this paper is to perform segmentation of white blood cells (WBCs) using blood smear images with the help of image processing techniques. Traditionally, the process of morphological analysis of cells is performed by a medical expert. This process is quite tedious and time consuming. The equipments used to perform the experiments are very costly and might not be available in all hospitals. Further, the whole process is quite lengthy and prone to error easily because of the lack of standard set of procedure. Hence there is a need for innovative and efficient techniques. An automated image segmentation system can make the blood test process much easier and faster. Segmentation of a nucleus image is one of the most critical tasks in a leukemia diagnosis. In this work, we have investigated and implemented image processing algorithms to segment cells. The proposed model detects WBCs and converts cell images from RGB to HSV color space using Otsu thresholding. The resultant image is then processed with the morphological filter because the segmented image contains noise which affects the system performance. Lastly, the Marker-based watershed algorithm is implemented in which specific marker positions are defined. The proposed model is tested on publically available ALL-IDB2 dataset. The system’s performance was overall examined and resulted in 98.99% overall precision for WBC segmentation.

An Automated Segmentation of Leukocytes Using Modified Watershed Algorithm on Peripheral Blood Smear Images

“…There are two sections of the database: ALL-IDB1 and ALL-IDB2. ALL-IDB2 dataset is developed from the All-IDB1 dataset containing 260 images that were previously utilised in our study [21]. Both datasets may be used to segment and categorise data.…”

DE-XRT coal preparation image overlapping and adhesion particle segmentation method

“…Marker-based watershed algorithm is widely used to perform more reasonable segmentation of the target. The commonly used marker-based watershed algorithms include morphological watershed (Ju et al, 2022), distance transform watershed (Guo et al, 2022), and extreme erosion watershed (Abrol et al, 2021). The difference between these three algorithms is that they adopt different target foreground marking methods.…”