Detection of rib shadows in digital chest radiographs. In: Del Bimbo A, editor. Image Analysis and Processing. Berlin: Springer Berlin Heidelberg; 1997. pp. 356-63. [DOI: 10.1007/3-540-63508-4_143]

Segmenting anatomy in chest x-rays for tuberculosis screening

In this paper we describe the development of a screening system for pulmonary pathologies (i.e. pneumonia, tuberculosis) application in global healthcare settings. As a first step toward this goal, the paper presents a novel approach for detecting lungs and ribs in chest radiographs. The approach is a unified method combining two detection schemes resulting in reduced cost. The novelty of our approach lies on the fact that instead of using pixel-wise techniques exclusively we used region-based features computed as wavelet features that take into consideration the orientation of anatomic structures. Initial results are described. Next steps include classification of non-rib lung regions for radiographic patterns suggesting tuberculosis infection.

Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification

The task of segmenting the posterior ribs within the lung fields of standard posteroanterior chest radiographs is considered. To this end, an iterative, pixel-based, supervised, statistical classification method is used, which is called iterated contextual pixel classification (ICPC). Starting from an initial rib segmentation obtained from pixel classification, ICPC updates it by reclassifying every pixel, based on the original features and, additionally, class label information of pixels in the neighborhood of the pixel to be reclassified. The method is evaluated on 30 radiographs taken from the JSRT (Japanese Society of Radiological Technology) database. All posterior ribs within the lung fields in these images have been traced manually by two observers. The first observer's segmentations are set as the gold standard; ICPC is trained using these segmentations. In a sixfold cross-validation experiment, ICPC achieves a classification accuracy of 0.86 +/- 0.06, as compared to 0.94 +/- 0.02 for the second human observer.

Evaluation and progression analysis of pulmonary tuberculosis from digital chest radiographs

Pulmonary tuberculosis is one of the most common infections in the third world countries. We propose a method for automated evaluation and progression analysis of this disease by processing a sequence of digital chest radiographs. The presence of radiological features characteristic to pulmonary tuberculosis is detected within the regions of interest using suitable image processing and pattern recognition techniques. The detected features are then quantified to evaluate the extent of the disease. The measures are evaluated for a sequence of radiographs of a patient and are normalized with respect to rib size measures. Temporal changes in these normalized measures indicate the progression of the disease during the period in which the sequence of radiographs is obtained. The details of the proposed technique and results of analysis of a number of tuberculous patients are given in the paper.