An Automated Computer-aided Diagnosis System for Abdominal CT Liver Images

AbstractIn this paper, we present a computer-aided diagnosis (CAD) system for abdominal Computed Tomography liver images that comprises four main phases: liver segmentation, lesion candidate segmentation, feature extraction from each candidate lesion, and liver disease classification. A hybrid approach based on fuzzy clustering and grey wolf optimisation is employed for automatic liver segmentation. Fast fuzzy c-means clustering is used for lesion candidates extraction, and a variety of features are extracted from each candidate. Finally, these features are used in a classification stage using a support vector machine. Experimental results confirm the efficacy of the proposed CAD system, which is shown to yield an overall accuracy of almost 96% in terms of healthy liver extraction and 97% for liver disease classification

CT liver tumor segmentation hybrid approach using neutrosophic sets, fast fuzzy c-means and adaptive watershed algorithm

Liver tumor segmentation from computed tomography (CT) images is a critical and challenging task. Due to the fuzziness in the liver pixel range, the neighboring organs of the liver with the same intensity, high noise and large variance of tumors. The segmentation process is necessary for the detection, identification, and measurement of objects in CT images. We perform an extensive review of the CT liver segmentation literature

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

K-mean Clustering for Segmentation of Irregular Shape Fruit Images under Various Illumination

Segmentation is the first step in analyzing or interpreting an image automatically. In particular applications, like image compression or image recognition, entire image can�t be processed directly. Hence many segmentation techniques are proposed to segment an image before processing it. This made it possible to develop many techniques which are currently using in different industries and agriculture field. They are either applied for grading or inspecting quality of food products and Fruits. These developed techniques use thresholding and clustering approach to get proper segmented output. In this paper an image segmentation approach is developed based on k-means adaptive clustering. This approach segments the various shape fruit images particularly which are non-circular (like banana, mango, and pineapple) and captured in various illumination such as low, Medium and high intensity. Earlier segmentation methods were not apposite for fruit images captured in natural light; as they were responsive to various colour intensity predisposed by the sunlight illumination. Natural illumination tempts an uneven amount of light intensity on the surface of the object, resulting in poor quality image segmentation. This approach will deal with problem of light effect. K-means clustering is renowned method for image segmentation. This method is more efficient, robust than the others. It provides best result when dataset is well separated and distinct. Different shape fruit images are segmented properly along with grey scale. The analytical results are the evidence for the accurate segmentation of banana, mango pineapple using new approach developed here

UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic.Â

A Fully Automatic Segmentation Method for Breast Ultrasound Images

Breast cancer is the second leading cause of death of women worldwide. Accurate lesion boundary detection is important for breast cancer diagnosis. Since many crucial features for discriminating benign and malignant lesions are based on the contour, shape, and texture of the lesion, an accurate segmentation method is essential for a successful diagnosis. Ultrasound is an effective screening tool and primarily useful for differentiating benign and malignant lesions. However, due to inherent speckle noise and low contrast of breast ultrasound imaging, automatic lesion segmentation is still a challenging task. This research focuses on developing a novel, effective, and fully automatic lesion segmentation method for breast ultrasound images. By incorporating empirical domain knowledge of breast structure, a region of interest is generated. Then, a novel enhancement algorithm (using a novel phase feature) and a newly developed neutrosophic clustering method are developed to detect the precise lesion boundary. Neutrosophy is a recently introduced branch of philosophy that deals with paradoxes, contradictions, antitheses, and antinomies. When neutrosophy is used to segment images with vague boundaries, its unique ability to deal with uncertainty is brought to bear. In this work, we apply neutrosophy to breast ultrasound image segmentation and propose a new clustering method named neutrosophic l-means. We compare the proposed method with traditional fuzzy c-means clustering and three other well-developed segmentation methods for breast ultrasound images, using the same database. Both accuracy and time complexity are analyzed. The proposed method achieves the best accuracy (TP rate is 94.36%, FP rate is 8.08%, and similarity rate is 87.39%) with a fairly rapid processing speed (about 20 seconds). Sensitivity analysis shows the robustness of the proposed method as well. Cases with multiple-lesions and severe shadowing effect (shadow areas having similar intensity values of the lesion and tightly connected with the lesion) are not included in this study

Machine Learning Algorithm for Early Detection and Analysis of Brain Tumors Using MRI Images

Among the human body's organs, the brain is the most delicate and specialized. It is proven that after the heart stops then also brain death occurs within 3 to 5 minutes of death or within 3 to 5 minutes of loss of oxygen supply. A brain tumor is a life-threatening disease that can be detected at any age from an infant to an old person. Though a lot of people did research in the detection and analysis of a tumor, but then also detecting tumors at the early phase is still a much more arduous field in the biomedical study. This paper focuses on the comparative study of various existing algorithms in this field. This paper addresses the challenges and some issues in MRI brain tumor detection which are also addressed in this research