Evaluation and progression analysis of pulmonary tuberculosis from digital chest radiographs1
Introduction
Tuberculosis has been one of the most dreadful diseases in the Indian subcontinent. Poor living conditions, malnutrition and compromised immune response are the main reasons for the spread of this infection. The symptoms and the nature of the pathology in pulmonary tuberculosis have been described in details in Refs 1, 2. It may be mentioned here that certain pathological tests must be performed to confirm the diagnosis. The chest X-rays are analyzed by radiologists primarily to evaluate the level of infection. The details about various radiological features of tuberculosis have been discussed in Refs 3, 4. The treatment for this disease is usually carried out for a period ranging from 9 to 18$months with regular radiological examinations to check for the prognosis of the disease. During the period of treatment, the lesions heal and the abnormal features in the chest radiograph gradually reduce and finally only traces of fibrosed tissue remain. Our effort, in this paper, lies in building a vision-based system by which the recovery rate of the disease for each patient can be quantified [5]. This is done by processing a sequence of digital chest radiographs of the same patient and detecting tuberculous features in these images and subsequently quantifying them. Our effort does not envisage an early detection of the disease as it is pathologically not possible to do so from chest radiographs alone. The analysis and quantification of the recovery rate of a patient can have significant clinical relevance as it would indicate whether or not the patient is responding to the treatment at a proper rate.
A computer-aided diagnosis system based on some quantitative criteria and a knowledge of all possible abnormal features to aid diagnosis has been a subject of research in the past few decades 6, 7, 8, 9, 10, 11, 12, 13. It has already been reported by researchers [7]that for some special cases this improves the overall diagnostic accuracy of a radiologist. However, no work on the detection of radiological features related to pulmonary tuberculosis and their quantification has been reported in the literature to the best of our knowledge.
There have been several efforts 14, 15, 16, 17to detect the rib structure in the image so that this knowledge of the rib locations can be used for further processing of the image. In Ref. [6]the authors investigate the problem of segmentation as a pattern classification problem. Work on computer-aided diagnosis of diseases relating to the detection of interstitial infiltrates has been reported in Refs 7, 8, 9, the detection of pulmonary nodules in Refs 10, 11, and the change analysis of the heart shadow and the associated vasculature in Ref. [12]. Most of these efforts concentrate on characterizing a certain class of radiographic features and do not treat any particular disease as a whole. In Ref. [13]Krueger et al. considered the disease pneumoconiosis but there has not been any subsequent effort in quantifying the disease.
Among various radiological features [4]that manifest due to tuberculosis, the most common features are infiltration and cavitation. In this work we have considered only these two features and the detection and the quantification schemes for them are outlined. Other features, such as fibrosis and calcification, can be additionally used for the quantification of the healing process and will be incorporated subsequently. The major problem in working with digital chest radiographs is the superimposed rib shadows within the lung field. Filtering techniques cannot easily get around this problem without the loss of some of the important information. The filtering technique used in Ref. [18]was initially attempted to remove the rib structure, but it leads to a significant loss of information. The enhancement technique as described in Ref. [19]does improve the contrast but the rib shadows also get enhanced. In this work we have adopted a data-driven algorithmic approach, where the radiologist's diagnostic routine is modeled by a sequence of computer algorithms.
The organization of the paper is as follows. In the next section, the initial processing of the chest radiographs to locate different anatomical regions of interest is discussed. Specifically, the lung fields are located in the image and are divided into three zones of interest. The rib spaces are located and a rib-subtracted lung field region is obtained for the detection of features. Methods of detection of the infiltration regions and cavitations are discussed in Section 3and Section 4, respectively. An iterative segmentation [20]is used to detect the regions of infiltration and subsequently to locate cavities, if any. In Section 5, we extract the quantitative measures for these features and analyze them to learn the recovery rate or the progression of the disease over time. Relevant results are shown in every section that substantiate the suitability of the proposed technique in building a system for aiding automated diagnosis. Section 6concludes the paper along with a discussion on the future scope of the work.
Section snippets
Pre-processing
A radiologist on his initial examination locates the normal landmarks of the chest image, namely the lung field consisting of the left and the right lungs, the heart shadow, shadows of the ribs, the thoracic cage boundary, the mediastinum and the diaphragm. This prescanning aids him to see if there is any gross abnormality and to locate the region of interest for examination. Subsequently, the radiologist will look for finer details in the region of interest. We proceed in a similar way to
Detection of infiltration
Tuberculous consolidation, usually called infiltration, has the following features.
Detection of cavitation
Cavities are the most common feature of chronic pulmonary tuberculosis. These indicate an active state of the disease and are a source of infectious tuberculous bacilli. In a chest radiograph, cavitation appears as a darker region surrounded by annular-shaped bright pixels which are similar in appearance to infiltration as described in the previous section. The shape and the size vary depending upon the extent of the diseased tissue. In most cases cavities are circular in shape. A fully formed
Progression analysis
The progression of the disease is evaluated from a sequence of radiographs of the same patient who is receiving a treatment for tuberculosis. Initially the pre-processing steps are carried out as described in earlier sections to obtain the lung field regions. The rib shadows are then detected and a rib shadow-subtracted image is obtained. Possible presence of infiltration and/or cavitation is also detected. The quantitative measures, namely the areas of the infiltration and the cavity are
Conclusion
Digital radiology has been a topic of research for the past few decades. Work has been done by various researchers in developing systems which will be able to improve the visual representation of the radiograph, to archive and to transmit images, to analyze digital radiographs using image processing and pattern recognition techniques, to detect radiological features and to make a diagnosis based on the analysis, if possible. All these are directed towards building an improved health care
Shantanu Sarkar was awarded his Bachelor of Technology degree in Electronics and Electrical Communication Engineering by the Indian Institute of Technology, Kharagpur, in June 1993. Subsequently he completed his Master of Technology degree in Biomedical Engineering at the Indian Institute of Technology, Bombay, in January 1997. Presently he is a graduate student in the Department of Biomedical Engineering at the University of Minnesota. His academic interests include image processing,
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Shantanu Sarkar was awarded his Bachelor of Technology degree in Electronics and Electrical Communication Engineering by the Indian Institute of Technology, Kharagpur, in June 1993. Subsequently he completed his Master of Technology degree in Biomedical Engineering at the Indian Institute of Technology, Bombay, in January 1997. Presently he is a graduate student in the Department of Biomedical Engineering at the University of Minnesota. His academic interests include image processing, instrumentation and quantitative analysis of medical imaging systems.
Subhasis Chaudhuri was born in Bahutali, India. He received his B.Tech. degree in Electronics and Electrical Communication Engineering from the Indian Institute of Technology, Kharagpur, in 1985. He received M.S. and the Ph.D. degrees, both in Electrical Engineering, from the University of Calgary, Canada, and the University of California, San Diego. He joined the IIT, Bombay, in 1990 and is currently serving as an associate professor. He has also served as a visiting professor at the University of Erlangen-Nuremberg, Germany, during the summer of 1996. He is a fellow of the Alexander von Humboldt Foundation. His research interests include image processing and computer vision, pattern recognition and biomedical signal processing.
- 1
Part of the paper was presented at the International Conference on Image Analysis and Processing, Sept. 1997, Florence, Italy.
- 2
Now with the Biomedical Engineering Institute, University of Minnesota, USA.