Detail publikace

Grading of Colorectal Cancer using Histology images

Originální název

Grading of Colorectal Cancer using Histology images

Anglický název

Grading of Colorectal Cancer using Histology images

Jazyk

en

Originální abstrakt

This paper presents an automated system for grading of colorectal cancer using image processing method. About, half a million people die every year due to colon cancer. Histopathological tissue analysis is a common method of its detection, which needs an expert pathologist. Screening for this cancer is effective for prevention as well as early detection. This proposed method segments the glands automatically by using intensity based thresholding and using organizational properties for classification. In existing literature, the majority of studies focus on gland segmentation in healthy or benign samples, but rarely on intermediate or high grade cancer. Unlike most of existing methods this system is fully automated and grades the images as benign healthy, benign adenomatous, moderately differentiated malignant and poorly differentiated malignant. The proposed method achieves overall accuracy of 81% when tested on 165 histology images

Anglický abstrakt

This paper presents an automated system for grading of colorectal cancer using image processing method. About, half a million people die every year due to colon cancer. Histopathological tissue analysis is a common method of its detection, which needs an expert pathologist. Screening for this cancer is effective for prevention as well as early detection. This proposed method segments the glands automatically by using intensity based thresholding and using organizational properties for classification. In existing literature, the majority of studies focus on gland segmentation in healthy or benign samples, but rarely on intermediate or high grade cancer. Unlike most of existing methods this system is fully automated and grades the images as benign healthy, benign adenomatous, moderately differentiated malignant and poorly differentiated malignant. The proposed method achieves overall accuracy of 81% when tested on 165 histology images

BibTex


@inproceedings{BUT128334,
  author="Namita {Sengar} and Neeraj {Mishra} and Malay Kishore {Dutta} and Jiří {Přinosil} and Radim {Burget}",
  title="Grading of Colorectal Cancer using Histology images",
  annote="This paper presents an automated system for grading of colorectal cancer using image processing method. About, half a million people die every year due to colon cancer. Histopathological tissue analysis is a common method of its detection, which needs an expert pathologist. Screening for this cancer is effective for prevention as well as early detection. This proposed method segments the glands automatically by using intensity based thresholding and using organizational properties for classification. In existing literature, the majority of studies focus on gland segmentation in healthy or benign samples, but rarely on intermediate or high grade cancer. Unlike most of existing methods this system is fully automated and grades the images as benign healthy, benign adenomatous, moderately differentiated malignant and poorly differentiated malignant. The proposed method achieves overall accuracy of 81% when tested on 165 histology images",
  booktitle="2016 39th International Conference on Telecommunications and Signal Processing (TSP)",
  chapter="128334",
  doi="10.1109/TSP.2016.7760936",
  howpublished="online",
  year="2016",
  month="june",
  pages="529--532",
  type="conference paper"
}