Classification of Lung CT Images using BRISK Features
B. Sambasivarao1, G Prathiba2
1B. Sambasivarao, PG Student, Department of ECE, Acharya Nagarjuna University, Guntur (Andhra Pradesh) India.
2Dr. G. Prathiba, Asst. Prof., Department of ECE, Acharya Nagarjuna University, Guntur (Andhra Pradesh) India.
Manuscript received on 18 April 2019 | Revised Manuscript received on 25 April 2019 | Manuscript published on 30 April 2019 | PP: 1187-1190 | Volume-8 Issue-4, April 2019 | Retrieval Number: D6682048419/19©BEIESP
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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: Lung cancer is the major cause of death in humans. To increase the survival rate of the people, early detection of cancer is required. Lung cancer that starts in the cells of lung is mainly of two types i.e., cancerous (malignant) and non-cancerous cell (benign). In this paper, work is done on the lung images obtained from the Society of Photographic Instrumentation Engineers (SPIE) database. This SPIE database contains normal, benign and malignant images. In this work, 300 images from the database are used out of which 150 are benign and 150 are malignant. Feature points of lung tumor images are extracted by using Binary Robust Invariant Scale Keypoints (BRISK). BRISK attains commensurate characteristic of correspondence at much less computation time. BRISK is adaptive, high quality accomplishments in avant-grade algorithms. BRISK features divide the pairs of pixels surrounding the keypoint into two subsets: short- distance and long-distance pairs. The orientation of the feature point is calculated by Local intensity gradients from long distance pairs. Rotation of Short distance pairs is obtained using this orientation. These BRISK features are used by classifier for classifying the lung tumors as either benign or malignant. The performance is evaluated by calculating the accuracy.
Keywords: Lung Cancer, CT Images, BRISK Features, Classification, Accuracy.
Scope of the Article: Classification