Paper Details

In order to improve the survival rate of lung cancer patient, early detection has a significantly more hopefulprognosis and is the key treatment. CT scanning presents great opportunities for lung cancer diagnosis. In this papermedian filter is used for image preprocessing. For image segmentation, thresholding and marker controlled Watershedsegmentation approach is used to segment the lung of CT image. In feature extraction step, Gray-level co-occurrencematrix is applied. Depending on the lung feature extraction, decision is made whether the lung has nodule or not.Diagnosis is mostly based on CT (computed tomography) images. CT scanning presents great opportunities for lungcancer diagnosis. The main objective of this paper is to implement lung nodule segmentation and feature extraction usingdigital image processing for the classification of the disease stages to avoid serious stages early and to reduce lungcancer percentage distribution. These images are more efficient and detailed than X-ray or other conventional methods.MATLAB is one of the most widely used computer program for the examination and study of CT scanned images. Thisprototype work proposes a convenient and low-cost procedure to detect the cancerous cells accurately from the capturedlung CT scanned images.

Apurva V. Ital, Dr.Mrs.S.O.Rajankar, “MEDICAL IMAGE SEGMENTATION AND DETECTION OF LUNG TUMOR ON CT SCAN IMAGES”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 5, Issue 8, pp. 101-107, August 2018.