Analysis of color images of tissues derived from patients with adenocarcinoma of the lung

This paper presents a method for automatic segmentation of lung tissue with color images to develop an efficient aided diagnosis system for adenocarcinoma of the lung based on the Hopfield neural network (HNN). The segmentation problem is formulated as minimization of an energy function synonymous to that of HNN for optimization. We modify the HNN to reach a status close to the global minimum in a prespecified time of convergence. The energy function is 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 to be close to the global minimum. Each lung color image is represented in RGB and HSV color spaces and the segmentation results are comparatively presented. Furthermore, the nuclei are automatically extracted based on the features of the color image histogram. The nuclei are the most component in the lung tissue.