Inverse maximum likelihood-based edge detection for segmentation of breast lesion using active contour

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values ​​of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

This chapter focuses on the development of novel image enhancement and robust edge detection methods for practical medical image processing. It is known that the popular transformation-domain shrinkage approach for image enhancement applies a fixed mathematical basis to transform all images to be processed for noise or artifact reduction. However it is not adaptable to processed images, and then easily leads to blurring in the enhanced images. On the other hand, the techniques that are commonly used for edge detection are known as gradient and Laplacian operators (or mask), and smoothed gradient masks are typically used for edge detection in noisy images. However, these methods share a common major drawback wherein the associated masks are always fixed irrespective of the noise level in the images. In this study, we propose a novel learning-based method to adaptively deduce the transforming basis or masks from the processing data for medical image enhancement and robust edge detection. By using independent component analysis (ICA), the proposed learning-based method can extract suitable basis functions or masks for image transformation for processing data, which are adaptable to both the processed image and related noise in the image. The efficiency of the proposed learning-based method for medical image enhancement and edge detection is demonstrated experimentally using positron emission tomography (PET) and magnetic resonance imaging (MRI) medical images.

The authors report the results of a controlled comparative study of four proposed methods for extracting boundaries in static images using color information. The first two techniques are, more properly speaking, edge-detection methods. These include the adaptation of the Hueckel operator to color images by R. Nevatia (1977) and the DOOG (difference of offset Gaussians) operator proposed by R. Young (1985, 1986). The latter two methods are examples of region-based segmentation. These include the adaptation of R. Ohlander et al. (1978), region splitting by Y. Ohta et al. (1980), and the fuzzy c-means segmentation and edge-detection methods of T.L. Huntsberger et al. (1985). The methods are compared using both simulated, controlled images as well as a variety of natural scene images. Images with various levels of randomly additive noise provide some indication of the degradation of performance of these methods under noisy conditions. >

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;

Chapter 3 - Segmentation

Spatial Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech

This study explores the application of digital image processing techniques to improve the diagnostic accuracy of coronary artery disease through enhanced analysis of angiographic images. A curated dataset of 40 grayscale, single-vessel angiograms was used to evaluate the effectiveness of various preprocessing and edge detection methods. Preprocessing steps included noise reduction, histogram equalization, and morphological operations aimed at improving image clarity and highlighting vascular structures. Three edge detection algorithms, Otsu, Canny, and Roberts, were applied, and their outputs were further combined using the Dempster-Shafer fusion theory. Performance was assessed using edge-based structural similarity (ESSIM) and the percentage error in vessel diameter measurements. The Canny algorithm demonstrated the highest individual accuracy, while the fusion of Canny and Roberts yielded superior results, achieving the lowest error and the highest similarity index.

Color Image Edge Detection Based on Fuzzy Rule Based Approach

Computerization has an essential role in various fields, one of which is remote sensing. His ability to process data quickly and accurately and receive and store data produces information based on a given program. Edge detection techniques in remote sensing imagery have developed rapidly in line with advances in computer technology. This technique is essential, one of which is used for preliminary surveys of coastline changes. A well-detected land will assist the surveyors in the field study. This study aims to determine three edge detection methods' performance, including Roberts' Filter, segmentation, and band ratio on Landsat 8 imagery in producing land edges directly adjacent to the sea area. The use of a 2x2 matrix on the Roberts' filter, the range of values ​​from 0.52 to 0.56 in the segmentation method, and the green band ratio - NIR Infrared resulted in varying accuracy. The results of this study show that the non-fusion image, the resulting edge is less sharp and coarse than the fusion image in the Roberts' filter method. The best method is produced by Band Ratio, where the edge detection results are close to the real image conditions. Meanwhile, unfavorable results were produced in the segmentation method. So that the band ratio has reliable performance for the detection of land objects bordering the ocean.

Cellular Learning Automata (CLA) has been used in many fields of image processing such as noise elimination, smoothing, retrieval, fractionated and extraction of the content Characteristics of the images. The edge detection in images and methods if edge detection, have a great role in machine vision and cognizance systems. This method uses operands for analyzing images and digital image processing. Many studios here been conducted till now in edge detection algorithms of various conditions. In this study a new method for edge detection in images with the use of CLA is recommended. The proposed method of edge detection in images was tested with different sizes and the results were compared with Sobel edge detector classic method. The result show that the method based on CLA has a desirable performance in edge detection and compares the images with a more uniformity during a minimum period of time.

One of the first steps of feature extraction is edge detection. There are various methods for edge detection such as sobel operator, log method and canny operator but with disadvantages (create noise, image smoothing and discontinuous edge detected). We present an improved method for edge detection. In this method, edge detected by morphology’s operator and their combination and with the use of various structure elements of images in satellite and remote sensing. Keywords: Satellite, Image processing, Edge detection, Multi spectral images

This paper proposes a image enhancement and segmentation method for mammographic images, which is based on the pixel intensity transformation, spatial filtering, edge detection, and region growing techniques. This method consists of two approaches. The first approach is applying intensity transformation, logarithmic transformation, contrast stretching, histogram equalization and spatial filtering techniques separately to enhance the mammogram images. In the second approach is applying edge detection operators, local and global thresholding techniques used to extract the suspicious regions from back ground tissue.

Microscopy of regenerated tissue shows different morphologies between the healing of acute wounds and chronic wounds. This difference can be seen manually by biologists, but computational methods are needed to automate the characterization of morphology and regenerative quality in regenerated muscle tissue. From the detected edge segments, we computed several imaging biomarkers of interest, such as median tortuosity, number of edge segments normalized by area, median edge segment distance and interquartile range of orientation angles of edge segments of the microscope images of successful and unsuccessful muscle regeneration. We observed that muscle fibers in saline-treated pressure ulcers had a larger interquartile range of orientation angles of the edge segments (p = 0.05) and shorter edge segment distances (p = 0.003) compared to those of acute cardiotoxin injuries. Our edge detection method was able to identify statistically significant differences in some of the imaging biomarkers between saline-treated pressure ulcers and cardiotoxin injuries, suggesting that chronic pressure ulcers have increased muscle fiber malformations compared to cardiotoxin injuries.

Edge detection is an important work for object recognition and is also an essential pre-processing step in image segmentation and visual analysis. This paper deeply studies the edge detection methods of the noisy images and concentrates on the mathematical morphology methods. By analyzing and studying the theory of mathematical morphology, the paper constructs edge detection operations and analyzes the specialties of the operator and the structure elements. Based on this, the paper proposes a new method of edge detection using mathematical morphology and image fusion. The operations can obtain clear and exact edge of the noisy images. By simulation and comparing with the traditional edge detection operations, the proposed method is more effective on noise restraining and retaining the image details.

Budaya aksara Lampung pada saat ini sudah jarang digunakan sehingga terancam punah. Untuk melestarikan budaya tersebut, maka dilakukan penelitian mengenai huruf aksara Lampung secara digital dengan metode pengolahan citra dan pengenalan pola. Pada penelitian ini, terbagi menjadi dua tahap yaitu tahap pelatihan dan tahap pengujian. Pada tahap pelatihan dilakukan pengolahan citra dan pelatihan jaringan saraf tiruan (JST) backpropagation. Salah satu tahapan dalam pengolahan citra adalah deteksi tepi. Dalam menganalisis karakter huruf aksara Lampung menggunakan metode deteksi tepi untuk melihat pola garis huruf tersebut. Metode pengolahan citra yang digunakan dalam mendeteksi tepi citra adalah metode deteksi tepi Roberts dan metode deteksi tepi Sobel. Hasil pengolahan citra akan masuk ke tahap pelatihan JST. Bobot yang didapat dari pelatihan kemudian akan digunakan pada tahap pengujian. Metode Sobel dapat mengenali karakter aksara Lampung lebih baik, dimana hasil dari pengujian karakter aksara Lampung memberikan nilai presentase error sebesar 28.5% dengan metode deteksi Roberts dan 14.5% dengan metode deteksi Sobel.