Automated visual inspection

Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition.

Improving medical image fusion method using fuzzy entropy and nonsubsampling contourlet transform

Blood cancer is the most prevalent and it is very much dangerous among all type of cancers. Early detection of blood cancer has the potential to reduce mortality and morbidity. There are many diagnostic technologies and tests to diagnose blood cancer. However many of these tests are extremely complex and subjective and depend heavily on the experience of the technician. To obviate these problems, image processing techniques and a fuzzy inference system is use in this study as promising modalities for detection of different types of blood cancer. The accuracy rate of the diagnosis of blood cancer by using the fuzzy system will be yield a slightly higher rate of accuracy then other traditional methods and will reduce the effort and time. We first discuss the preliminary of cell biology required to proceed to implement our proposed method. This paper presents a new automated approach for blood Cancer detection and analysis from a given photograph of patient's cancer affected blood sample. The proposed method is using Wavelet Transformation for image improvement, image segmentation for segmenting the different cells of blood, edge detection for detecting the boundary, size, and shape of the cells and finally Fuzzy Inference System for Final decision of blood cancer based on the number of different cells.

Automated Blood Cancer Detection Using Image Processing Based on Fuzzy System

Edge detection is a primary operation of most of the image processing applications such as image detection, boundary detection, image classification, image registration. Edge detection filters out less important information and preserve the structural properties of image.The Proposed technique uses ANFIS edge detector for edge detection on digital images. It involves a neuro fuzzy system with the learning capability of neural network and the advantages of rule- based fuzzy system. This work follows hybrid algorithm to resolve the edge detection issues with the help of least square method and gradient descent method.

Edge Detection Using Soft Computing in Matlab

Computerized systems for eye maladies distinguishing proof are significant in the ophthalmology field. Conservative systems for the detection of eye disease depend on labour-intensive awareness of the retinal segments. This work exhibits another directed technique for hemorrhages discovery in advanced retinal images. This strategy utilizes an ANFIS plot for pixel association and registers a 5-D vector made out of dim dimension and Cross Section Profie (CSP) Study-constructed highlights for pixel portrayal. Classification of diseases is a crucial aspect in eye disease categorization through image processing techniques. The categorization of diseases according to pathogen groups is a significant research domain and potentially a challenging area of work. Various classification techniques for single as well as multiple diseases is identified. Classification and detection are very similar, but in classification primary focus is on the categorization of various diseases and then the classification according to various pathogen groups.

Retinal Image Classification System Using Multi Phase Level Set Formulation and ANFIS

Edge detection is a critical element in image processing, since edges contain a major function of image information. The function of edge detection is to identify the boundaries of homogeneous regions in an image based on properties such as intensity and texture. Many edge detection algorithms have been developed based on computation of the intensity gradient vector, which, in general, is sensitive to noise in the image. In order to suppress the noise, the operator based on fuzzy technique is introduced in order to simulate at a mathematical level the compensatory behavior in process of decision making or subjective evaluation. The following paper introduces such operators on hand of computer vision application. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for edge detection in digital images without determining the threshold value. The proposed approach begins by segmenting the images into regions using floating 3×3 binary matrix. The edge pixels are mapped to a range of values distinct from each other. The robustness of the proposed method results for different captured images are compared to those obtained with the linear Sobel operator. It is gave a permanent effect in the lines smoothness and straightness for the straight lines and good roundness for the curved lines. In the same time the corners get sharper and can be defined easily.

J. H. M. Ajitha

Papers

Fuzzy information system based digital image segmentation by edge detection

Testing and analysis of nanoparticles-based textrodes for physiological signals