Adaptive Intuitionistic Fuzzy Enhancement of Brain Tumor MR Images

A new method of contrast enhancement of musculoskeletal ultrasound imaging based on fuzzy inference technique

Improving the clarity and visual quality of Musculoskeletal Ultrasound Images (MUI) can help clinicians to detect diseases more easily and accurately. In this work, we described how to enhance the contrast of MUI locally based on a fuzzy inference system. Local Fuzzy Inference Technique (LFIT) was introduced as a novel technique to enhance the contrast of MUI. The input data used musculoskeletal ultrasound images were collected from healthy volunteers. Local Fuzzy Inference Technique (LFIT) was compared with a recent fuzzy technique of the image enhancement and validated based on assessment metrics (second-derivative-like measure of enhancement (SDME)). The results advocated an improved quality of the musculoskeletal ultrasound images based on the LFIT technique with approximately 11% greater than recent technique and computation time of LFIT is 28.4% is less. It is possible to apply a proposal technique on the other types of image (panoramic image and video). Furthermore, observed improvements on the MUI quality could potentially invested as a pre-processing step before performing other computer vision applications, such as image segmentation, tracking, and 3D image reconstruction.

Intuitionistic based segmentation of thyroid nodules in ultrasound images

Accurate delineation of thyroid nodules in ultrasound images is vital for computer-aided diagnosis. Most segmentation methods are semi-automated for thyroid nodules and require manual intervention, which increases the processing time and errors. We propose an automated intuitionistic fuzzy active contour method (IFACM) that integrates intuitionistic fuzzy clustering with an active contour for thyroid nodule segmentation using ultrasound images. Intuitionistic fuzzy clustering is used for the initialization of an active contour and estimation of the parameters required to automatically control the curve evolution. The IFACM was tested extensively on both artificial and real ultrasound images. The IFACM obtained a higher value of true positive (95.1% ± 2.86%), overlap metric (93.1 ± 2.95%), and dice coefficient (90.90 ± 3.08), indicating that the boundary delineated by the IFACM fits best to true nodules. Moreover, it obtained a lower value of false positive (04.1% ± 3.24%) and Hausdorff distance (0.50 ± 0.21 in pixels), further verifying the higher similarity of shape and boundary, respectively. According to the significance test, the results of the proposed method were more significant than those of the other segmentation methods. The main benefit of the IFACM is the automatic identification of nodules on the basis of image characteristics, which eliminates manual intervention. In all the experiments, all initial contours were automatically defined closer to the boundaries of the nodule, which is a benefit of the IFACM. Moreover, this method can segment multiple nodules in a single image efficiently.

An adaptive image enhancement method for a recirculating aquaculture system

Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination. Then, the normalized incomplete Beta function is used to perform a greyscale nonlinear transformation. The function's optimal parameters (α and β) are automatically selected by the particle swarm optimization (PSO) algorithm based on an image contrast measurement function. This adaptive image enhancement method is compared with other classic enhancement methods. The results show that the proposed method greatly improves the image contrast and highlights dark areas, which is helpful during further analysis of these images.