Novel Method of CT Chest Image Segmentation and Analysis for Early Lung Cancer Detection
By: Kumbhar, Vinod B
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Contributor(s): Chavan, Mahesh S.
Publisher: New York Springer 2022Edition: Vol, 103(6), Dec.Description: 1875-1883p.Subject(s): Electrical EngineeringOnline resources: Click here
In:
Journal of the institution of engineers (India): Series BSummary: Lung cancer is one of the most prominent and deadly diseases across the world. However, early detection of this disease has proved very useful in increasing the survival rate of the affected patients. With the development of sophisticated image processing algorithms, computer-aided diagnosis (CAD) is evolving as one of the most efficient methods for detecting lung cancer. CT scanning technique is routinely applied for diagnosing lung cancer. These CT scan images are checked by radiologists who comment on the presence or absence of cancer. However, CT scan images of an early-stage cancer are very hard to be identified with naked eyes. In this scenario, the CAD plays a major role to assist the radiologists. The four steps of images processing techniques employed in CAD are—(1) reading the image, (2) preprocessing or filtering the image, (3) morphological processing that involves erosion, dilation, etc., and (4) detecting a cancerous region in an image and improving the cancerous region's status as a candidate for true or false positive status. The proposed method in this work uses efficient image segmentation algorithms that help to enhance the accuracy of the diagnosis.
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2022-2339 |
Lung cancer is one of the most prominent and deadly diseases across the world. However, early detection of this disease has proved very useful in increasing the survival rate of the affected patients. With the development of sophisticated image processing algorithms, computer-aided diagnosis (CAD) is evolving as one of the most efficient methods for detecting lung cancer. CT scanning technique is routinely applied for diagnosing lung cancer. These CT scan images are checked by radiologists who comment on the presence or absence of cancer. However, CT scan images of an early-stage cancer are very hard to be identified with naked eyes. In this scenario, the CAD plays a major role to assist the radiologists. The four steps of images processing techniques employed in CAD are—(1) reading the image, (2) preprocessing or filtering the image, (3) morphological processing that involves erosion, dilation, etc., and (4) detecting a cancerous region in an image and improving the cancerous region's status as a candidate for true or false positive status. The proposed method in this work uses efficient image segmentation algorithms that help to enhance the accuracy of the diagnosis.
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