An adaptive image segmentation process for the classification of lung biopsy images

Daniel W. McKee; Walker H. Land Jr.; Tatyana Zhukov; Dansheng Song; Wei Qian

doi:10.1117/12.650105

15 March 2006 An adaptive image segmentation process for the classification of lung biopsy images

Daniel W. McKee, Walker H. Land Jr., Tatyana Zhukov, Dansheng Song, Wei Qian

Proceedings Volume 6144, Medical Imaging 2006: Image Processing; 614452 (2006) https://doi.org/10.1117/12.650105
Event: Medical Imaging, 2006, San Diego, California, United States

Abstract

The purpose of this study was to develop a computer-based second opinion diagnostic tool that could read microscope images of lung tissue and classify the tissue sample as normal or cancerous. This problem can be broken down into three areas: segmentation, feature extraction and measurement, and classification. We introduce a kernel-based extension of fuzzy c-means to provide a coarse initial segmentation, with heuristically-based mechanisms to improve the accuracy of the segmentation. The segmented image is then processed to extract and quantify features. Finally, the measured features are used by a Support Vector Machine (SVM) to classify the tissue sample. The performance of this approach was tested using a database of 85 images collected at the Moffitt Cancer Center and Research Institute. These images represent a wide variety of normal lung tissue samples, as well as multiple types of lung cancer. When used with a subset of the data containing images from the normal and adenocarcinoma classes, we were able to correctly classify 78% of the images, with a ROC A_Z of 0.758.

Citation Download Citation

Daniel W. McKee, Walker H. Land Jr., Tatyana Zhukov, Dansheng Song, and Wei Qian "An adaptive image segmentation process for the classification of lung biopsy images", Proc. SPIE 6144, Medical Imaging 2006: Image Processing, 614452 (15 March 2006); https://doi.org/10.1117/12.650105

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