Rachid Sammouda

Lung cancer detection by using artificial neural network and fuzzy clustering methods

The early detection of the lung cancer is a challenging problem, due to the structure of the cancer cells. This paper presents two segmentation methods, Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) clustering algorithm, for segmenting sputum color images to detect the lung cancer in its early stages. The manual analysis of the sputum samples is time consuming, inaccurate and requires intensive trained person to avoid diagnostic errors. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer which will improves the chances of survival for the patient. The two methods are designed to classify the image of N pixels among M classes. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM, the HNN succeeded in extracting the nuclei and cytoplasm regions.

A comparison of Hopfield neural network and Boltzmann machine in segmenting MR images of the brain

The segmentation of the images obtained from magnetic resonance imaging is an important step in the visualization of soft tissues in the human body. In this preliminary study, we report an application of the Hopfield neural network for the multispectral unsupervised classification of head magnetic resonance images. We formulate the classification problem as a minimization of an energy function constructed with two terms, the cost-term which is the sum of the squares errors, and the second term is a temporary noise added to the cost-term as an excitation to the network to escape from certain local minimums and be more close to the global minimum. We present here the segmentation result with two and three channels data obtained using the Hopfield neural network approach. We compare these results to those corresponding to the same data obtained with the Boltzmann machine approach.

Agriculture satellite image segmentation using a modified artificial Hopfield neural network

Beekeeping plays an important role in increasing and diversifying the incomes of many rural communities in Kingdom of Saudi Arabia. However, despite the regions relatively good rainfall, which results in better forage conditions, bees and beekeepers are greatly affected by seasonal shortages of bee forage. Because of these shortages, beekeepers must continually move their colonies in search of better forage. The aim of this paper is to determine the actual bee forage areas with specific characteristics like population density, ecological distribution, flowering phenology based on color satellite image segmentation. Satellite images are currently used as an efficient tool for agricultural management and monitoring. It is also one of the most difficult image segmentation problems due to factors like environmental conditions, poor resolution and poor illumination. Pixel clustering is a popular way of determining the homogeneous image regions, corresponding to the different land cover types, based on their spectral properties. In this paper Hopfield neural network (HNN) is introduced as Pixel clustering based segmentation method for agriculture satellite images.

Identification of Lung Cancer Based on Shape and Color

The analysis of sputum color images can be used to detect the lung cancer in its early stages. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid high errors. Image processing techniques provide a good tool for improving the manual screening of sputum samples. In this paper two basic techniques have been applied: a region detection technique and a feature extraction technique with the aim to achieve a high specificity rate and reduce the time consumed to analyze such sputum samples. These techniques are based on determining the shape of the nuclei inside the sputum cells. After that we extract some features from the nuclei shape to build our diagnostic rule. The final results will be used for a computer aided diagnosis (CAD) system for early detection of lung cancer.

Morphology analysis of sputum color images for early lung cancer diagnosis

Lung cancer is a serious illness which control is mainly based on presumptive diagnosis. Besides of clinical suspicion, the diagnosis of lung cancer must be done through specific smears of sputum specimens. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid high errors. Image processing techniques provide a good tool for improving the manual screening of sputum samples. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection of lung cancer based on the analysis of sputum color images with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a region detection technique and a feature extraction technique to determine the shape of the nuclei inside the sputum cells. The final results will be used for a (CAD) system for early detection of lung cancer.

Liver cancer detection system based on the analysis of digitized color images of tissue samples obtained using needle biopsy

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. Furthermore, as the segmentation of color images does not only depend on the segmentation algorithm but also on the color space representation, and in order to choose the best segmentation result, segmentation was performed with HNN and using components of the raw image with respect to each of the RGB, HLS, and HSV color spaces. Then, the segmented image was labeled based on chromaticity features and histogram analysis of the RGB color space components of the raw image. The image regions were then classified into normal and cancerous using diagnostic rules formulated based on those used by experienced pathologists in the clinic. The proposed method provides quantitative satisfactory results in diagnosing a liver pathological image set of 17 cases.

A content based image retrieval using K-means algorithm

The study of Medical image retrieval, which is concerned with efficiently and effectively accessing desired medical images from varied and large image collections, has become more important, challenging and interesting. Until now extracting the lesions by automatic segmentation is considered the bottleneck in content-based medical image retrieval. Above that many approaches which are based on one-to-one blocks of an image are still sensitive to rotation, shifting and scaling. To address the last problems, we propose a new approach based on Hungarian algorithm which compares one block from the query image to all blocks from each image in the dataset and returns the closet matching. This comparison is based on features vector of gray-level intensity for each block. We used K-mean clustering algorithm to separate each window into K clusters. We will enforce the histogram of each cluster into Gaussian distribution, and then based on this histogram, the mean, variance and skewness are computed. The proposed content-based medical image retrieval approach gives acceptable results.

Tissue color images segmentation using artificial neural networks

Currently, most pathologists make their diagnosis of cancer based on a rough estimation of the density of the cells nuclei in the tissue sample, and also based on the morphological abnormality of the cancerous cells. The methods used to achieve their diagnosis are either too simple to diagnose a complicated tissue image or are depending on heavy human intervention and very time consuming. In order to assist pathologists to make a consistent, objective and fast diagnosis, we present in this paper a method of tissue color image segmentation as the main step of an entire system of cancer diagnosis. The segmentation approach is an unsupervised algorithm based on a modified Hopfield neural network (HNN). This algorithm is superior to HNN in the sense that it converges in a prespecified time to a nearby global minimum rather than an early local minimum. Two types of tissue (liver, lung) are presented, and three-color spaces (RGB, HLS and HSV) are used to investigate the efficiency of the algorithm in segmenting color images.

Segmentation and analysis of liver cancer pathological color images based on artificial neural networks

Liver cancer is one of those sneaky conditions that can disappoint a physician before the diagnosis is finally made. Thus far, the only definitive test for liver cancer is needle biopsy. In this paper, we present an unsupervised approach using Hopfield neural network for the segmentation of color images of liver tissues prepared and stained by standard staining method. We formulate the segmentation problem as a minimization of an energy function synonymous to that of Hopfield neural network for the optimization, with the addition of some conditions to reach a status close to the global minimum in a prespecified time of convergence. Then we extract the nuclei and their corresponding cytoplasm regions which are used as a base for formulating the diagnostic rules of a computer aided diagnosis system for liver cancer. In computer, each liver color image is represented in the R-G-B, H-S-V and H-L-S color spaces and the segmentation results are comparatively presented with discussion and physician comments. Most of the data base of liver color images that we have collected have been successfully segmented with the exception of some images which were not stained carefully.

Improving the performance of Hopfield Neural Network to segment pathological liver color images

Abstract In this paper, we propose an original contribution to improve the segmentation of multidimensional medical color images using the Hopfield Neural Network (HNN) classifier. In our previous work, the segmentation problem has been formulated as an energy function composed of two terms: the sum of squared errors and a noise term to help HNN in its minimization process of the segmentation problems energy function to reach a local minimum close to the global minimum. Here, we demonstrate that considering the sum of a higher weighted error than the sum of squared errors leads the HNN classifier to go more deeply in the energy landscape. The proposed system is evaluated on 20 pathological liver color images and shown to be efficient and very effective in making crisp segmentation of the data set.