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Lung Cancer Diagnosis of CT-SCAN Images Using Watershed Transform

S. O. Rajanakar, Apurva V. Ital


The lung cancer is one of the biggest cause of death by cancer. If the cancer is detected in early stage, it can help us with variety of treatments, cheap surgery options and decreases death rate of patient(s). If detection of lung cancer is done within time, chances of death due to this can be decreased. The death rate of patient decreases from 49% to 14%. It is considered one of the most dangerous and widely-spread disease in whole world. The cancer disease is caused by cancer cells in lungs of the patient. Detection of these cells is biggest issue faced by medical researchers. If these cells are detected earlier, chances of more effective treatment will significantly increase. In this prototype, the Computed Tomography (CT) images are used. 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. This prototype work proposes a convenient and low-cost procedure to detect the cancerous cells accurately from the captured lung CT scanned images. These images are processed by various technique(s) which includes CT scanned image pre-processing and segmentation, feature extraction and classification. This will minimize human error and increase accuracy in detection.


Computed Tomography (CT), Image Processing, MATLAB2013a, Segmentation.

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