Posterior capsule opacification (PCO) is the most common complication of cataract surgery, occurring in up to 50% of patients by 2–3 years after the operation [Spalton in Eye 13(Pt 3b):489–492, 1999]. This paper proposes a new approach for the assessment of PCO digital images. The approach deploys an unsupervised learning technique for clustering image pixels into different regions based on chromatic attributes.

The initial weight matrix to be used in the unsupervised hopfield neural network (HNN) image based classification or segmentation has a strong influence in the quality of the solution obtained after a specified time or iteration number given to the network to find the best solution and converge. An inadequate initial random matrix may cause the classifier to start with a big sum of errors in its first distribution of pixels among a pre-decided number of clusters, and get stack in a poor local minimum far from the global optima.

This paper describes a method for an automatic extraction of lung regions from 3D-CT images using pure basic image processing techniques. This extraction process should be as much as possible accurate and reliable because its results will be used as a base to develop a Computer Aided Diagnosis (CAD) system for lung cancer. First, each 2D slice is converted to a set of binary images using bit-plane slicing technique instead of the thresholding technique that is used in most of the proposed systems.

In this article, the authors propose a method for automatic diagnosis of liver cancer based on analysis of digitized color images of liver tissue obtained by needle biopsy. The approach is a combination of an unsupervised segmentation algorithm, using a modified artificial Hopfield neural network (HNN), and an analysis algorithm based on image quantization. The segmentation algorithm is superior to HNN in the sense that it converges to a nearby global minimum rather than a local one in a prespecified time.