Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradientbased operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters

Edge Detection Techniques for Rice Grain Quality Analysis using Image Processing Techniques

In agricultural countries like the Philippines, rice grain is considered the most important crop in the world for human consumption as daily food and in the food market, thus quality control must be considered. Rice grain quality evaluation is done manually, which is non-reliable, time-consuming and costly. The quality of rice grain is categorized by the combination of physical and chemical characteristics. Grain appearance, color, size and shape, chalkiness, whiteness, degree of milling, bulk density, foreign matter content, and moisture content are some physical characteristics, while amylose content of the endosperm, gelatinization temperature of the endosperm starch, and Na content are chemical characteristics. This paper presents a solution for the grading and evaluation of rice grains on the basis of grain size and shape using Scilab Image Video Progressing (SIVP) techniques. Specifically, an edge detection algorithm is used to find out the region of the boundaries of each grain. This method requires a minimum of time and is more affordable. Edge detection is vital for its reliability and security, as well as for providing a better understanding of automatic identification in computer vision applications. This study determines the best techniques among the edge detection algorithms

Vectorization of human pelvis objects in X-ray images

In medical diagnostics visual evaluation of an object or its image is necessary but time consuming operation. Well-known computer vision algorithms or their compilation, or even some new methods should be the right tool in increasing the speed and reliability of this process. This paper introduces situation in this domain and some experiments and their results in extraction of biomechanical parameters of human pelvis from x-ray images using combination of Hough transform for a line, for a circle (arc) and Canny edge detector. The main idea of an algorithm, which was created during this experiment, is to use different levels of noise filter thus making a balance between leaving too much noise and removing too much actual data. The basic steps would be: filter out most of noise and noisy objects using high filter’s threshold value; find sharp and clear objects; narrow the set of possible parameters of noisy objects; apply noise filter with lower threshold value to the original image; find noisy objects. Experiment shows that algorithm works but it needs to be tested on reliability and some bindings with actual biomechanical parameters should be done (see [1–6])

New Edge Detection Method Using Elisabeth Method: Case Study Javanese Batiks

Abstract. This article will introduce a new edge detection method called Elisabeth method to analyze image. The case study here is Javanese Batik’s motif. Edges are basic low level primitives for image processing. It helps to identify pictures. Methods used are the combination between Sobel and Prewitt. This method is completely new to analyze Javanese Batik’s motif. Every batik motif has unique pattern. The purpose of this research is to improving edge detection method that already known now. The result is a new method in edge detection problems. Batik is one of the Indonesian Heritage that avowed as a Heritage World Cultures. With this research it hoped can help our country to classify and identify Batik’s motif items in Indonesia. Keywords: Prewitt, Sobel, Elisabeth, Javanese Batik, Parang, Kawung Abstrak. Metode Baru Deteksi Tepi Menggunakan Metode Elisabeth: Studi Kasus Batik Jawa. Artikel ini akan memperkenalkan sebuah metode baru deteksi tepi yang disebut dengan metode Elisabeth untuk menganalisis citra. Studi kasus yang digunakan disini adalah motif Batik Jawa. Tepi adalah primitif level dasar untuk pemrosesan citra. Ini membantu mengidentifikasi gambar. Metode yang digunakan adalah kombinasi antara Sobel dan Prewitt. Metode ini benar-benar baru untuk menganalisis motif Batik Jawa. Setiap motif batik memiliki pola yang unik. Tujuan dari penelitian ini adalah untuk meningkatkan metode deteksi tepi yang sudah dikenal sekarang. Hasilnya adalah metode baru dalam masalah deteksi tepi. Batik adalah salah satu Warisan Indonesia yang diakui sebagai Warisan Budaya Dunia. Dengan penelitian ini diharapkan dapat membantu negara kita untuk mengklasifikasikan dan mengidentifikasi motif Batik di Indonesia. Kata Kunci: Prewitt, Sobel, Elisabeth, Batik Jawa, Parang, Kawun

AI Machine Vision based Oven White Paper Color Classification and Label Position Real-time Monitoring System to Check Direction

We develop a vision system for batch inspection by oven white paper model color by manufacturing a machine vision system for the oven manufacturing automation process. In the vision system, white paper object detection (spring), color clustering, and histogram extraction are performed. In addition, for the automated process of home appliances, we intend to develop an automatic mold combination detection algorithm that inspects the label position and direction (angle/coordinate) using deep learning

Statistically Representative Metrology of Nanoparticles via Unsupervised Machine Learning of TEM Images

The morphology of nanoparticles governs their properties for a range of important applica tions. Thus, the ability to statistically correlate this key particle performance parameter is paramount in achieving accurate control of nanoparticle properties. Among several effective techniques for morphological characterization of nanoparticles, transmission electron microscopy (TEM) can pro vide a direct, accurate characterization of the details of nanoparticle structures and morphology at atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this work, we demonstrate an efficient, robust and highly automated unsupervised machine learning method for the metrology of nanoparticle systems based on TEM images. Our method not only can achieve statistically significant analysis, but it is also robust against variable image quality, imaging modalities, and particle dispersions. The ability to efficiently gain statistically significant particle metrology is critical in advancing precise particle synthesis and accurate property control.Australia Research Council (ARC) IC210100056Ministerio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-