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

Abstract

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.

Keywords: Bone X-ray; Bone cancer; Connected component; Decision tree; Ortho-convex cover; Runs-test; Support vector machine.

Fig. 1

Block diagram of the proposed technique

Fig. 2

Bone contour generation. a Bone X-ray image. b E-S image of (a). c Single-pixel contour of (b)

Fig. 3

aX-ray of cancer-affected bone with ragged surface, b Healthy bone X-ray. c Variation of number of connected components on bone surface with total component size (pixels) for healthy and cancer-affected bone images

Fig. 4

Randomness detection of intensity. a Bone X-ray image. b Heterogeneous pixel-intensity distribution (colored pixels) detected via runs-test

Fig. 5

Staircase in an ortho-convex polygon. a Rising stair. b Falling stair. c Ortho-convex cover

Fig. 6

a Bone X-ray image with ragged surface. b Ortho-convex cover for“ragged” bone surface. c Bone X-ray image with hollow surface. d Ortho-convex cover for “hollow” bone surface

Fig. 7

Cancer-affected bones. a Geographic pattern. b Moth-eaten pattern. c Permeative pattern. d Calcification. e Giant cyst

Fig. 8

Decision tree for classifying bone-destruction patterns

Fig. 9

SVM (binary) for bone cancer diagnosis (points in rectangle represent misclassification)

Fig. 10

Stage and grade classification using decision tree (points in rectangle represent misclassification)

Fig. 11

(a) Column-1, column-3, and column-5: input X-ray image. (b) Column-2, column-4, and column-6: ortho-convex cover of affected region with heterogeneous pixels marked by runs-test

Fig. 12

a Mean ROC curve for bone cancer detection (using SVM). b Mean ROC curve for cancer stage and grade detection (using decision tree). c Mean ROC curve for bone-destruction pattern detection (using decision tree)