Bone Fracture Lab Report
Abstract
Bones protect our organs from potential damage. The fracture can occur in any bone of the body like wrist, ankle, hip, rib, leg, chest etc. This Paper Present the detection and segmentation of bone fracture using Fuzzy-c Means and Multilevel wavelet algorithm. Bone Fracture detection has become a research area in the medical imaging system. Bone fracture detection helps in finding the fracture in the various body parts like hand, leg, and rib fracture etc. The system is consisting of four stages to detect and segment a fracture. An efficient algorithm is proposed for bone fracture based on thresholding and fuzzy C-Means segmentation and morphological operators. Firstly remove the noise from the image and then …show more content…
A fracture can be crosswise, lengthwise, in several places, or into small or two or more pieces. Typically, a bone becomes fractured by more force or pressure or fall off bikes or climbing frames. Doctors can identify the fractures by examining the injury and taking X-rays. Sometimes an X-ray will not show a fracture in wrist, hip, and stress fractures. In these situations, doctor performs other tests, like ultrasound, computed tomography scan, magnetic resonance imaging , Endoscopy, Medical photography or a scan of the bones. [9]. In the existing technique, they have to detect the appearance of rheumatoid arthritis. The first step is to denoise the image; by using the median filter the noise can be removed. The next step is to normalize the image through the histogram smoothing and used the segmentation by using the thresholding. Then the morphological operation of dilation and erosion to allow removal of the bone area named as a region of interest. The next step is boundary detection, to find the edges of the bones by using the canny edge detection method. If the values of a diagnosis are above than a specific range than rheumatoid arthritis can be positive. For the classification, the Neural Network can be used. The input image is pushed into the BMD Block, and then all the steps are performed on the input image. Then extract the GLCM features of the input image. Then the values can be calculated by using the neural network and then classify whether the input image is infected or not
Bones protect our organs from potential damage. The fracture can occur in any bone of the body like wrist, ankle, hip, rib, leg, chest etc. This Paper Present the detection and segmentation of bone fracture using Fuzzy-c Means and Multilevel wavelet algorithm. Bone Fracture detection has become a research area in the medical imaging system. Bone fracture detection helps in finding the fracture in the various body parts like hand, leg, and rib fracture etc. The system is consisting of four stages to detect and segment a fracture. An efficient algorithm is proposed for bone fracture based on thresholding and fuzzy C-Means segmentation and morphological operators. Firstly remove the noise from the image and then …show more content…
A fracture can be crosswise, lengthwise, in several places, or into small or two or more pieces. Typically, a bone becomes fractured by more force or pressure or fall off bikes or climbing frames. Doctors can identify the fractures by examining the injury and taking X-rays. Sometimes an X-ray will not show a fracture in wrist, hip, and stress fractures. In these situations, doctor performs other tests, like ultrasound, computed tomography scan, magnetic resonance imaging , Endoscopy, Medical photography or a scan of the bones. [9]. In the existing technique, they have to detect the appearance of rheumatoid arthritis. The first step is to denoise the image; by using the median filter the noise can be removed. The next step is to normalize the image through the histogram smoothing and used the segmentation by using the thresholding. Then the morphological operation of dilation and erosion to allow removal of the bone area named as a region of interest. The next step is boundary detection, to find the edges of the bones by using the canny edge detection method. If the values of a diagnosis are above than a specific range than rheumatoid arthritis can be positive. For the classification, the Neural Network can be used. The input image is pushed into the BMD Block, and then all the steps are performed on the input image. Then extract the GLCM features of the input image. Then the values can be calculated by using the neural network and then classify whether the input image is infected or not