Fatma Taher

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.

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.

Extraction and Segmentation of Sputum Cells for Lung Cancer Early Diagnosis

Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its symptoms can be found exclusively in advanced stages where the chances for patients to survive are very low, thus making the mortality rate the highest among all other types of cancer. The present work deals with the attempt to design computer-aided detection or diagnosis (CAD) systems for early detection of lung cancer based on the analysis of sputum color images. The aim is to reduce the false negative rate and to increase the true positive rate as much as possible. The early detection of lung cancer from sputum images is a challenging problem, due to both the structure of the cancer cells and the stained method which are employed in the formulation of the sputum cells. We present here a framework for the extraction and segmentation of sputum cells in sputum images using, respectively, a threshold classifier, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive techniques via a series of experimentation conducted with a data set of 100 images. The extraction and segmentation results will be used as a base for a CAD system for early detection of lung cancer which will improve the chances of survival for the patient.

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.

Bayesian classification and artificial neural network methods for lung cancer early diagnosis

Lung cancer is a serious illness which can be cured if it is diagnosed at early stages. One technique which is commonly used for early detection of this type of cancer consists of analyzing sputum images. However, the analysis of sputum images is time consuming and requires highly trained personnel to avoid diagnostic errors. Image processing techniques provide a reliable tool for improving the manual screening of sputum samples. In this paper, we address the problem of extraction and segmentation the sputum cells based on the analysis of sputum color image 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 use a Bayesian classifier to extract the sputum cells followed by using a Hopfield Neural Network (HNN) to segment the extracted cells into nuclei and cytoplasm regions from the background region. The final results will be used for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer. We used some performance criteria such as sensitivity, specificity and accuracy to evaluate the proposed methods. Our methods are validated via a series of experimentation conducted with a data set of 88 images.

Cell extraction from sputum images for early lung cancer detection

The context of this work is the design of Computer-Aided Design system for the early detection of lung cancer using digital sputum images. This process requires the detection of the sputum cell region in the sputum image characterized by a cluttered background. In this paper we address this problem using two different methods, namely, a Rule-based method, and Bayesian classification. We describe the two methods and we compare their performances in terms of their behaviors with respect to color representation and color quantization.

Detection and segmentation of sputum cell for early lung cancer detection

Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its early detection significantly increases the chances of an effective treatment. For this purpose, a computer-aided design system using images of sputum stained smears is a practical, low-cost, and totally non invasive solution. In this paper, we present a framework for the detection and segmentation of sputum cells in sputum images using respectively, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive approaches via a series of experiments conducted with a data set of 88 images.

A new multi watermarking algorithm for medical images using DWT and hash functions

In this paper, we propose a blind digital multi- watermarking algorithm for the copyright protection and authentication of medical images. When medical images are transmitted and stored in hospitals, strict security, confidentiality and integrity are required to protect from the illegal distortion and reproduction of the medical information. Medical image watermarking requires extreme care when embedding watermark information in the medical images because the additional information must not affect the image quality as this may cause the wrong diagnosis. The proposed algorithm contains one robust watermark for the ownership of the image and two fragile watermarks for checking the authenticity. The first two watermarks are embedded in the wavelet domain, while the third watermark is embedded in the spatial domain. In the proposed algorithm, the medical image is divided into two regions, called the Region of Interest and Region of Non Interest and all the three watermarks are embedded in the Region of Non-Interest. The new medical watermarking technique offers high peak signal to noise ratio and similarity structure index measure values. The technique was successfully tested on a variety of medical images and the experimental results show that the robust watermark survived many intentional and non intentional attacks, while the fragile watermark is sensitive to any slight tampering on the medical images.

Segmentation of sputum color image for lung cancer diagnosis based on mean shift algorithm

This paper presents the mean shift segmentation algorithm for segmenting the extracted sputum cells into nuclei and cytoplasm regions. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer. The mean shift is a mode seeking process on a surface design with a kernel. Also it will be used as a strategy to perform multistart global optimization. The histogram analysis is used to find the best distribution of the nuclei and cytoplasm sputum cell pixels and to find the best color space that can be used to perform the mean shift segmentation. The Mena shift method offers better performance compared to other segmentation algorithm including Hopefield Neural Network (HNN). The new method is validated on a set of manually defined ground truths sputum images.

Artificial neural network and fuzzy clustering methods in segmenting sputum color images for lung cancer diagnosis

Lung cancer is cancer that starts in the lungs. Cancer is a disease where cancerous cells grow out of control, taking over normal cells and organs in the body. The early detection of lung cancer is the most effective way to decrease the mortality rate. In this paper we compare two methods, a modified Hopfield Neural Network (HNN) and a Fuzzy C-Mean (FCM) Clustering Algorithm, used in segmenting sputum color images. The segmentation results will be used as a base for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer. Both methods are designed to classify the image of N pixels among M classes or regions. Due to intensity variations in the background of the raw images, a pre-segmentation process is developed to standardize the segmentation process. In this study, we used 1000 sputum color images to test both methods, and HNN has shown a better classification result than FCM; however the latter was faster in converging.