Study of Ultrasound Based Imaging System for Cancer Detection

Abstract

Before the advent of medical imaging, palpation was one of the malO methods to detect abnormalities in the body, mainly because the mechanical properties of diseased tissue are typically different than that of the healthy tissue surrounding it. Utilizing the same concept of palpation now ultrasound elasticity imaging technique is a promising new tool for cancer diagnosis and management. Ultrasound is applied to sense small local tissue deformations noninvasively to image stiffness and thus exploit the large intrinsic stiffness contrast generated during the progression of many diseases. Elasticity (strain) images are generally computed by measuring the local deformation due to the controlled application of static tissue compressive force. Local deformation is estimated by two techniques. One is correlation technique and another is compression technique. Correlation based strain imaging is efficient in cancer detection for its simplicity and lower cost but large displacement estimation error occurs in this technique. Therefore, in our thesis we investigate direct compression technique for strain imaging that provide significantly better image for cancer detection. But compression technique has a limitation in cancer detection which is computational cost. Computational cost is higher in compression technique. To overcome this limitation we apply frame wise compression technique which made the algorithm faster. In our work, we construct an elasticity image from the radio frequency ultrasound signals acquired before and after applying a physical pressure in the region of interest. We observe that, normal B-mode image cannot detect the cancer or hard tissue surrounding by normal tissue. On the other hand, strain image can detect cancer or hard tissue surrounding by normal tissue. For this reason people are trying to incorporate strain imaging technique in ultrasound equipment commercially. Therefore strain/elasticity imaging is now showing potential in early cancer detection.