Estimation of object location probability for object detection using brightness feature only

Most existing object detection methods use features such as color, shape, and contour. If there are no consistent features can be used, we need a new object detection method. Therefore, in this paper, we propose a new method for estimating the probability that an object can be located for object detection and generating an object location probability map using only brightness in a gray image. To evaluate the performance of the proposed method, we applied it to gallbladder detection. Experimental results showed 98.02% success rate for gallbladder detection in ultrasonogram. Therefore, the proposed method accurately estimates the object location probability and effectively detected gallbladder

Defect Detection in Ceramic Images Using Sigma Edge Information and Contour Tracking Method

In this paper, we suggest a method of detecting defects by applying Hough transform and least squares on ceramic images obtained from non-destructive testing. In the ceramic images obtained from non-destructive testing, the background area, where the defect does not exist, commonly shows gradual change of luminosity in vertical direction. In order to extract the background area which is going to be used in the detection of defects, Hough transform is performed to rotate the ceramic image in a way that the direction of overall luminosity change lies in the vertical direction as much as possible. Least squares is then applied on the rotated image to approximate the contrast value of the background area. The extracted background area is used for extracting defects from the ceramic images. In this paper we applied this method on ceramic images acquired from non-destructive testing. It was confirmed that extracted background area could be effectively applied for searching the section where the defect exists and detecting the defect

Fully automatic segmentation of intima/adventitia of the vessel using Bezier curve from intravascular ultrasound

Although medical image segmentation field is regarded as one of most established fields, still fully automatic segmentation to extract target object with high accuracy from intravascular ultrasound (IVUS) is very active area of research. In this paper, we propose a fully automatic morphological approach using Bezier curve in interpolating the boundaries of intima/adventitia of the vessel from IVUS with careful binarization algorithms. In experiment with 800 IVUS images, the proposed method is as good as fuzzy C-means based approach in comparison with human expert’s result with 84.4% satisfaction and better than other morphological method in all performance indices of curve fitting with 97.02% in accuracy and 58.19% in precision

Intelligent Automatic Extraction of Canine Cataract Object with Dynamic Controlled Fuzzy C-Means based Quantization

Canine cataract is developed with aging and can cause the blindness or surgical treatment if not treated timely. Since the pet owner do not have professional knowledge nor professional equipment, there is a growing need of providing pre-diagnosis software that can extract cataract-suspicious regions from simple photographs taken by cellular phones for the sake of preventive public health. In this paper, we propose a software that is highly successful for that purpose. The proposed software uses dynamic control of FCM clusters in quantification and trapezoid membership function in fuzzy stretching in order to enhance the intensity contrast from such rough photograph input. Through experiment, the proposed system demonstrates sufficiently enough accuracy in extraction (successful in 42 out of 45 cases) with better quality comparing with previous attempt

Developing an automatic brachial artery segmentation and bloodstream analysis tool using possibilistic C-means clustering from color doppler ultrasound images

Automatic segmentation of brachial artery and blood-flow dynamics are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose a software that is noise tolerant and fully automatic in segmentation of brachial artery from color Doppler ultrasound images. Possibilistic C-Means clustering algorithm is applied to make the automatic segmentation. We use HSV color model to enhance the contrast of bloodstream area in the input image. Our software also provides index of hemoglobin distribution with respect to the blood flow velocity for pathologists to proceed further analysis. In experiment, the proposed method successfully extracts the target area in 59 out of 60 cases (98.3%) with field expert’s verification

Effective Computer-Assisted Automatic Cervical Vertebrae Extraction with Rehabilitative Ultrasound Imaging by using K-means Clustering

Neck pain is one of most common musculoskeletal condition resulting in significant clinical, social and economic costs. Muscles around cervical spine including deep neck flexors play a key role to support and control its stability, thus monitoring such muscles near cervical vertebrae is important. In this paper, we propose a fully automated computer assisted method to detect cervical vertebrae with K-means pixel clustering from ultrasonography. The method also applies a series of image processing algorithms to remove unnecessary organs and noises in the process. The experiment verifies that our approach is consistent with human medical experts’ decision to locate key measuring point for muscle analysis and successful in detecting cervical vertebrae accurately – successful in 48 out of 50 test cases (96%)

Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Automatic segmentation of wrist bone fracture area by K-means pixel clustering from X-ray image

Early detection of subtle fracture is important particularly for the senior citizens’ quality of life. Naked eye examination from X-ray image may cause false negatives due to operator subjectivity thus computer vision based automatic detection software is much needed in practice.  In this paper, we propose an automatic extraction method for suspisious wrist fracture regions. We apply K-means in pixel clustering to form the candidate part of possible fracture from wrist X-ray image automatically. This method can recover previously detected patterned false cases with edge detection method after fuzzy stretching. The proposed method is successful in 16 out of 20 tested cases in experiment