Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network

Rachid Sammouda; Noboru Niki; Hiroshi Nishitani; S. Nakamura; Shinichiro Mori

doi:10.1117/12.274177

25 April 1997 Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network

Rachid Sammouda, Noboru Niki, Hiroshi Nishitani, S. Nakamura, Shinichiro Mori

Proceedings Volume 3034, Medical Imaging 1997: Image Processing; (1997) https://doi.org/10.1117/12.274177
Event: Medical Imaging 1997, 1997, Newport Beach, CA, United States

Abstract

The paper presents a method for automatic segmentation of sputum cells with color images, to develop an efficient algorithm for lung cancer diagnosis based on a Hopfield neural network. We formulate the segmentation problem as a minimization of an energy function constructed with two terms, the cost-term as a sum of squared errors, and the second term a temporary noise added to the network as an excitation to escape certain local minima with the result of being closer to the global minimum. To increase the accuracy in segmenting the regions of interest, a preclassification technique is used to extract the sputum cell regions within the color image and remove those of the debris cells. The former is then given with the raw image to the input of Hopfield neural network to make a crisp segmentation by assigning each pixel to label such as background, cytoplasm, and nucleus. The proposed technique has yielded correct segmentation of complex scene of sputum prepared by ordinary manual staining method in most of the tested images selected from our database containing thousands of sputum color images.

Citation Download Citation

Rachid Sammouda, Noboru Niki, Hiroshi Nishitani, S. Nakamura, and Shinichiro Mori "Unsupervised sputum color image segmentation for lung cancer diagnosis based on a Hopfield neural network", Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); https://doi.org/10.1117/12.274177

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