Sputum Images Research Articles

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.

Automatic Sputum Color Image Segmentation for Lung Cancer Diagnosis

Lung cancer is considered to be the leading cause of cancer death worldwide. A technique commonly used consists of analyzing sputum images for detecting lung cancer cells. However, the analysis of sputum is time consuming and requires highly trained personnel to avoid errors. The manual screening of sputum samples has to be improved by using image processing techniques. In this paper we present a Computer Aided Diagnosis (CAD) system for early detection and diagnosis of lung cancer based on the analysis of the sputum color image with the aim to attain a high accuracy rate and to reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we present a framework for segmentation and extraction of sputum cells in sputum images using respectively, a Bayesian classification method followed by region detection and feature extraction techniques 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. We analyzed the performance of a Bayesian classification with respect to the color space representation and quantification. Our methods were validated via a series of experimentation conducted with a data set of 100 images. Our evaluation criteria were based on sensitivity, specificity and accuracy.

Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models

Tuberculosis and other kinds of mycobacteriosis are serious illnesses for which early diagnosis is critical for disease control. Sputum sample analysis is a common manual technique employed for bacillus detection but current sample-analysis techniques are time-consuming, very tedious, subject to poor specificity and require highly trained personnel. Image-processing and pattern-recognition techniques are appropriate tools for improving the manual screening of samples. Here we present a new technique for sputum image analysis that combines invariant shape features and chromatic channel thresholding. Some feature descriptors were extracted from an edited bacillus data set to characterize their shape. They were statistically represented by using a Gaussian mixture model representation and a minimal error Bayesian classification procedure was employed for the last identification stage. This technique constitutes a step towards automating the process and providing a high specificity.

Identification of tuberculosis bacteria based on shape and color

Tuberculosis and other mycobacteriosis are serious illnesses which control is based on early diagnosis. A technique commonly used consists of analyzing sputum images for detecting bacilli. 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 samples. In this paper, a new autofocus algorithm and a new bacilli detection technique is presented with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. This technique is based on the combined use of some invariant shape features together with a simple thresholding operation on the chromatic channels. Some feature descriptors have been extracted from bacilli shape using an edited dataset of samples. A k -means clustering technique was applied for classification purposes and the sensitivity vs specificity results were evaluated using a standard ROC analysis procedure.

Automatic segmentation and identification of the Mycobacterium tuberculosis in sputum images

Tuberculosis is a very serious disease whose control is based on early diagnosis. A method frequently employed in its diagnosis consists of the sputum analysis in order to detect the Mycobacterium tuberculosis. The sputum examination demands a great amount of time and a good training of the specialist is required to avoid to commit a great numbers of errors. Image processing techniques can be helpful in examinations. Thus, this paper presents a new technique, it tries to improve the precision and diminish the time used in the analysis sputum samples. This techniques uses the linguistic knowledge about the characteristics of the bacilli, using the color information for segmentation and a classification tree for bacilli identification to establishing if a sample is positive or negative.