Diagnosis of Benign and Malignant Renal Tumors Based on Multi-Feature Sparse Constraints

Considering that the kidneys segmentation challenge for image processing because of the gray level from abdominal computer tomography (CT) scans is a great similarity of adjacent organs, partial volume effects and so on, a novel multi-feature sparse constraints strategy is proposed to diagnose the benign and malignant renal tumors, which can improve the accuracy and reliability of segmentation. The weighted sparse measure is defined by introducing weights in the l1-norm of vectors. The weight is inversely proportional to the similarity between data, therefore the weighted l1-norm penalty on the linear representation coefficients tends to force similar data be involved while dissimilar data uninvolved in the linear representation of a datum. The resulted representation can overcome the drawbacks of l1-norm penalty that the presentation coefficients are usually over sparse and not robust for highly correlated data. Experimental results and objective assessment indexes show that the proposed method can effectively segment CT images with good visual consistency. In addition, the dice coefficients of renal and renal tumors were 0.933 and 0.854, respectively. In addition, our method can also be used for the diagnosis of renal tumors, and has also achieved good performance.