Open Access

ARTICLE

Automatic Liver Tumor Segmentation in CT Modalities Using MAT-ACM

S. Priyadarsini^1,*, Carlos Andrés Tavera Romero², Abolfazl Mehbodniya³, P. Vidya Sagar⁴, Sudhakar Sengan⁵

1 Department of Computer Science and Engineering, P.S.R. Engineering College, Sivakasi, Tamil Nadu, 626123, India
2 COMBA R&D Laboratory, Faculty of Engineering, Universidad Santiago de Cali, Cali, 76001, Colombia
3 Department of Electronics and Communications Engineering, Kuwait College of Science and Technology, Kuwait
4 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, 522502, India
5 Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, 627152, India

* Corresponding Author: S. Priyadarsini. Email:

Computer Systems Science and Engineering 2022, 43(3), 1057-1068. https://doi.org/10.32604/csse.2022.024788

Received 31 October 2021; Accepted 01 December 2021; Issue published 09 May 2022

Abstract

In the recent days, the segmentation of Liver Tumor (LT) has been demanding and challenging. The process of segmenting the liver and accurately spotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of the liver create difficulties during liver segmentation. The manual segmentation does not provide an accurate segmentation because the results provided by different medical experts can vary. Also, this manual technique requires a large number of image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-Angle Texture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from its neighbouring soft tissues with related strength. Then, the filtered images are given as the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection (CED) technique, which segments the Liver Region (LR) from the given CT images. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error (VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applying the MAT-ACM, which accurately segments the LR from the segmented LRs. The evaluation of the proposed method is compared with the existing LTS methods using various performance measures to prove the superiority of the proposed MAT-ACM method.

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