Screening and Identify the Bone Cancer/Tumor using Image Processing

Medical imaging is playing an imperative function in analysis and healing of disease and locating tumours and finding of cancerous cells in premature phase. As a traditional approach for identifying bone features, is microscopic images were used. These images are acquired by using micro radiography, where it needed to repeated, time consuming and labor intensive process. This technique is unable to identify the cancerous cells because of the presence of noise in the images. Hence there is a need for automated and reliable techniques to carry out the image processing analysis. As a first stage, the most basic part of image processing is to denoising without interrupting the diagnostics information during the removal of noise. The earlier process removes the noise and introduce blur in the image. In order to get precise image processing, we have implemented soft and hard threshold with various coefficients and to measure the threshold Visu shrink was used. It was found that the Wavelet deionsing tool was a powerful tool for image enhancement. In the session, our proposed work was associated with pre-processing techniques in order to remove the noise and to get smooth images. This process will help to improve the quality of the image and also eliminate the false segments. In order to detect the existence of bone cancer and to determine its stage, K- means algorithm was used and subsequently to get smooth picture, edge segmentation process was performed. The principle component of GA analysis, distinguish between the benign and malignant growth of the bone tumor. Our research focus was mainly to predict or detect the bone tumor on right time and stage of the bone tumor. With our approach that is image processing and genetic algorithm were used to detect bone tumor effectively without any false interpretation, which would subsequently help therapists for proper treatment.