Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification
Lung Lesion Detection in CT Scan Images Using the Fuzzy Local Information Cluster Means (FLICM) Automatic Segmentation Algorithm and Back Propagation Network Classification
Asian Pac J Cancer Prev. 2017 Dec 29;18(12):3395-3399
Authors: Lavanya M, Kannan PM
Abstract
Lung cancer is a frequently lethal disease often causing death of human beings at an early age because of uncontrolled
cell growth in the lung tissues. The diagnostic methods available are less than effective for detection of cancer. Therefore
an automatic lesion segmentation method with computed tomography (CT) scans has been developed. However it is
very difficult to perform automatic identification and segmentation of lung tumours with good accuracy because of
the existence of variation in lesions. This paper describes the application of a robust lesion detection and segmentation
technique to segment every individual cell from pathological images to extract the essential features. The proposed
technique based on the FLICM (Fuzzy Local Information Cluster Means) algorithm used for segmentation, with
reduced false positives in detecting lung cancers. The back propagation network used to classify cancer cells is based
on computer aided diagnosis (CAD).
PMID: 29286609 [PubMed - as supplied by publisher]
Source: Asian Pacific Journal of Cancer Prevention - Category: Cancer & Oncology Tags: Asian Pac J Cancer Prev Source Type: research