Fuzzy C-Means and Antlion Optimization Based Segmentation of Juxtapleural Lung Nodules
Parvathi P1, Rajeswari R2

1Parvathi P, Department of Computer Applications, Bharathiar University, Coimbatore, India.
2Rajeswari R, Department of Computer Applications, Bharathiar University, Coimbatore, India.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1041-1048 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6270018520/2020©BEIESP | DOI: 10.35940/ijrte.E6270.018520

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: A Computer aided detection of lung nodules plays a vital role in diagnosis of lung cancer. The aim of this paper is to utilize the characteristics of hybrid Fuzzy C-Means-Ant lion Optimization (FCM-ALO) and morphological operations to extract the juxtapleural lung nodules. The hybrid FCM-ALO based clustering helps in isolating the nodules and boundaries of lung lobes. Morphological operations are then applied to isolate the juxtapleural nodules from the lung boundaries. The proposed method is evaluated using 28 computed tomography (CT) case studies from Lung Imaging Database Consortium-Image Database Resource Initiative (LIDC-IDRI) with 100 juxtapleural nodules. The FCM-ALO based clustering approach gives 0.9464, 0.1575 and 0.2009 as average silhouette index (S), Davies-Bouldin index (DB) and entropy respectively. The sensitivity, specificity and accuracy of the proposed juxtapleural lung nodule segmentation are 99.5%, 95.03%, and 97.63% respectively.
Keywords: Antlion Optimization, Fuzzy-C-Means, Morphological Operators, Juxtapleural Nodules, Segmentation.
Scope of the Article: Fuzzy logics.