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Improving the Performance of CAD System for Diagnosis of Diseases from CT Images


Author(s):

T. Primya , Dr.N.G.P Institute of Technology; G. Kanagaraj, Kumaraguru College of Technology; G. Selva priya, Dr.N.G.P Institute of Technology; V. Suresh, Dr.N.G.P Institute of Technology

Keywords:

Computer Aided Detection (CAD), Grey Level Coocurrence Matrix (GLCM), k-Nearest Neighbour

Abstract:

Pulmonary embolism (PE) is an extremely common and highly lethal condition that is a leading cause of death in all age groups. CAD is increasingly used in clinical practice to assist physicians in the detection of subtle abnormalities. Over the past 10 years, computed tomography (CT) scanners have gained acceptance as a minimally invasive method for diagnosing PE. In this Paper Computer-aided detection (CAD) scheme is developed for PE detection. It includes Lung segmentation, Candidate Extraction, Feature Extraction, classification, performance Evaluation. Segmentation is done by means of adaptive threshold and Region growing process. In Candidate Extraction tobogganing algorithm was applied to detect and extract PE candidates by removing the thoracic, mediastinum and other nonlung areas. In Feature Extraction the features are extracted by using Grey Level co-occurrence Matrix. Then the extracted features are to be stored in the database. Classification is done by means of k-Nearest Neighbor classifier which is used to find True Positive and False positive. Then the performance evaluation is measured by using Receiver Operating Characteristics (ROC) curve.


Other Details:

Manuscript Id :IJSTEV3I1175
Published in :Volume : 3, Issue : 1
Publication Date: 01/08/2016
Page(s): 406-412
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