Bone-Cancer Assessment and Destruction Pattern Analysis in Long-Bone X-ray Image.

Bone cancer originates from bone and rapidly spreads to the rest of the body affecting the patient. A quick and preliminary diagnosis of bone cancer begins with the analysis of bone X-ray or MRI image. Compared to MRI, an X-ray image provides a low-cost diagnostic tool for diagnosis and visualization of bone cancer. In this paper, a novel technique for the assessment of cancer stage and grade in long bones based on X-ray image analysis has been proposed. Cancer-affected bone images usually appear with a variation in bone texture in the affected region. A fusion of different methodologies is used for the purpose of our analysis. In the proposed approach, we extract certain features from bone X-ray images and use support vector machine (SVM) to discriminate healthy and cancerous bones. A technique based on digital geometry is deployed for localizing cancer-affected regions. Characterization of the present stage and grade of the disease and identification of the underlying bone-destruction pattern are performed using a decision tree classifier. Furthermore, the method leads to the development of a computer-aided diagnostic tool that can readily be used by paramedics and doctors. Experimental results on a number of test cases reveal satisfactory diagnostic inferences when compared with ground truth known from clinical findings.

Long-bone fracture detection in digital X-ray images based on digital-geometric techniques.

Automated fracture detection is an essential part of a computer-aided tele-medicine system. In this paper, we have proposed a unified technique for the detection and evaluation of orthopaedic fractures in long-bone digital X-ray image. We have also developed a software tool that can be conveniently used by paramedics or specialist doctors. The proposed tool first segments the bone region of an input digital X-ray image from its surrounding flesh region and then generates the bone-contour using an adaptive thresholding approach. Next, it performs unsupervised correction of bone-contour discontinuities that might have been generated because of segmentation errors, and finally detects the presence of fracture in the bone. Moreover, the method can also localize the line-of-break for easy visualization of the fracture, identify its orientation, and assess the extent of damage in the bone. Several concepts from digital geometry such as relaxed straightness and concavity index are utilized to correct contour imperfections, and to detect fracture locations and type. Experiments on a database of several long-bone digital X-ray images show satisfactory results.