Mini Kirsch Edge Detection and Its Sharpening Effect

In computer vision, edge detection is a crucial step in identifying the objects’ boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true non-edge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods.  The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy

An approach for cross-modality guided quality enhancement of liver image

A novel approach for multimodal liver image contrast enhancement is put forward in this paper. The proposed approach utilizes magnetic resonance imaging (MRI) scan of liver as a guide to enhance the structures of computed tomography (CT) liver. The enhancement process consists of two phases: The first phase is the transformation of MRI and CT modalities to be in the same range. Then the histogram of CT liver is adjusted to match the histogram of MRI. In the second phase, an adaptive histogram equalization technique is presented by splitting the CT histogram into two sub-histograms and replacing their cumulative distribution functions with two smooths sigmoid. The subjective and objective assessments of experimental results indicated that the proposed approach yields better results. In addition, the image contrast is effectively enhanced as well as the mean brightness and details are well preserved

Design and Implementation of 2D Spatial Filter for EEG and MRI Segmentation

The Electroencephalography (EEG) of brain field continues to be an attractive tool in clinical practice due to its real time depiction of brain function .The aim of this paper is to give a review of digital image segmentation technique .This paper study and implements the different types of 2D spatial filter(weighted, smoothening ,derivative) for EEG segmentation . Paper focuses on developing an automated system to enhance and recover the corrupted EEG signal images and MRI images with the help of 2D spatial filter and it also helps in early and accurate diagnosis of brain tumour. It ensures fast and reliable detection and formal resolution of deformed images by implementing noise addition and removal, edge detection, cropping, histogram adjustment, scale conversion as required by the image. DOI: 10.17762/ijritcc2321-8169.15053

Detection of Retinal Disease Using Image Processing

India is one of the countries which is emerging in the field of telemedicine in recent years. We are still far away from our desired goal. To add to that the patients with eye diseases are also increasing rapidly. To provide them with a better treatment at a lower price is the main goal. The people in urban areas still manage an eye checkup but for the people in rural areas it becomes difficult. Mobile phones are reaching to every nook and corner of the country with the help of that telemedicine becomes possible. We want to come up with a solution in which this becomes possible. It is applied on image processing and machine learning. Image processing is having significance for disease detection on medical images. With help of image processing and machine learning techniques it is possible to automate and/or assist physicians in clinical diagnosis. This project synopsis describes the application of various image processing and machine learning techniques for detection of eye diseases. Data is the future of technology. With the technological revolution the amount of data is increasing rapidly in any field. Thus using this data to distinguish between two images becomes our primary goal. The preprocessing technique leads to enhance the boundaries and feature extraction process and along with conversion of image type and then by combining the image processing part with the machine learning part we are able to design the algorithm. For this we are using concept of Template Matching template is nothing but a sub image which is small. The goal is to find similarities in template and input image. Due to this idea process will be done easily at faster rat