COMPARISON OF SEGMENTATION METHODS USED FOR BONE FRACTURE IMAGES

The usage of computers and software in the biomedical field has been increasing and applications for doctors, clinicians, scientists and other users have been developed in the recent times. Manual, semi-automatic and fully automatic applications developed for bone fracture detection are one of the important studies in this field. Image segmentation, which is one of the image preprocessing steps in bone fracture detection, is an important step to obtain successful results with high accuracy. In this study, Otsu thresholding method, active contour method, k-means method, fuzzy c-mean method, Niblack thresholding method and max min thresholding range (MMTR) method are used on bone images obtained by Karabük University Training and Research Hospital. When any filters are not applied on images to remove noises, the most successful method is obtained by K-means method based on specificity and accuracy as 89,55% and 83,31% respectively. Niblack thresholding method has the highest sensitivity result as 92,45%.

Semi-Automatic Image Segmentation on X-ray Image of Spine using Active Contour Method

Currently, many image analysis methods have been developed on X-Ray of scoliotic patients. However, segmentation of spinal curvature is still a challenge, and needs to be improved. In this research, we proposed a semi-automatic spinal image segmentation of scoliotic patients from X-Ray images. This method is divided into 2 steps: preprocessing and segmentation process. A conversion process from RGB to grayscale and CLAHE (Contrast Limited Adequate Histogram Equalization) method was used in image preprocessing. The active contour method was used for the segmentation process. The result shows that segmentation of spinal X-ray images of scoliotic patients using active contour method interactively, can give better results. The average of ME and RAE values are 12.98% and 26.75 %. instead of using the interactive region splitting method which gets 21.17% and 89.27%. Keywords: active contour, interactive segmentation, pre-processing, scoliosis.

Preliminary process in blast cell morphology identification based on image segmentation methods

The diagnosis of blood disorders in developing countries usually uses the diagnostic procedure Complete Blood Count (CBC). This is due to the limitations of existing health facilities so that examinations use standard microscopes as required in CBC examinations. However, the CBC process still poses a problem, namely that the procedure for manually counting blood cells with a microscope requires a lot of energy and time, and is expensive. This paper will discuss alternative uses of image processing technology in blast cell identification by using microscope images. In this paper, we will discuss in detail the morphological measurements which include the diameter, circumference and area of blast cell cells based on watershed segmentation methods and active contour. As a basis for further development, we compare the performance between the uses of both methods. The results show that the active contour method has an error percentage of 5.15% while the watershed method has an error percentage of 8.25%.

Automatic Segmentation and Classification of Liver Tumor using Hybrid Neural Network

Liver tumor is most common nowadays. Liver tumor segmentation is one of the most essential steps in treating it. We have chosen CT scan image for liver tumor diagnosis. Accurate tumor segmentation is done using computed tomography (CT) images. Since the manual identification is not that much accurate and time consuming, we go for active contour method. This automatic segmentation method is highly accurate and provides very less time for computation. The back propagation classifier method has a very good accuracy rate and a very less error rate and hence achieved the best result. The proposed method we used in this paper is back propagation classification algorithm for the detection of early and final stages of liver tumor. For the automatic segmentation, we use an active contour method to segment the liver and liver tumor to overcome the manual segmentation problem. This is an automatic method will help us to know whether the tumor is in benign or malignant stage.