New chain-codes algorithm of image contour in medical image process

2002 ◽  
Author(s):  
Yu Zhang ◽  
Wenyao Liu ◽  
Wei Zheng ◽  
Yongjie Hao ◽  
Jintao Wang
Keyword(s):  
Medical Image ◽  
Image Process ◽  
Chain Codes ◽  
Image Contour

scholarly journals Nonrigid Registration of Lung CT Images Based on Tissue Features

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Rui Zhang ◽  
Wu Zhou ◽  
Yanjie Li ◽  
Shaode Yu ◽  
Yaoqin Xie
Keyword(s):  
Image Registration ◽  
Medical Image ◽  
Feature Detection ◽  
Ct Images ◽  
Image Process ◽  
Scale Invariant ◽  
Nonrigid Image Registration ◽  
Breathing Motion ◽  
Key Points ◽  
High Gradients

Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration.

scholarly journals A novel approach to jointly address localization and classification of breast cancer using bio-inspired approach

2019 ◽  
Vol 9 (2) ◽  
pp. 992
Author(s):  
Sushma S. J. ◽  
S. C. Prasanna Kumar
Keyword(s):  
Breast Cancer ◽  
Medical Image ◽  
Optimization Technique ◽  
Generalized Solution ◽  
Image Process ◽  
Classification Problems ◽  
Novel Approach ◽  
Research Gap

Localization of the cancerous region as well as classification of the type of the cancer is highly inter-linked with each other. However, investigation towards existing approaches depicts that these problems are always iindividually solved where there is still a big research gap for a generalized solution towards addressing both the problems. Therefore, the proposed manuscript presents a simple, novel, and less-iterative computational model that jointly address the localization-classification problems taking the case study of early diagnosis of breast cancer. The proposed study harnesses the potential of simple bio-inspired optimization technique in order to obtained better local and global best outcome to confirm the accuracy of the outcome. The study outcome of the proposed system exhibits that proposed system offers higher accuracy and lower response time in contrast with other existing classifiers that are freqently witnessed in existing approaches of classification in medical image process.

scholarly journals SlicerArduino: A Bridge between Medical Imaging Platform and Microcontroller

2020 ◽  
Vol 7 (3) ◽  
pp. 109
Author(s):  
Paolo Zaffino ◽  
Alessio Merola ◽  
Domenico Leuzzi ◽  
Virgilio Sabatino ◽  
Carlo Cosentino ◽  
...  
Keyword(s):  
Medical Image ◽  
Image Process ◽  
Input Output ◽  
Real Time Control ◽  
3D Slicer ◽  
Time Control ◽  
Communication Layer ◽  
Deep Integration ◽  
Hardware Platforms ◽  
Basic Input

Interaction between medical image platform and external environment is a desirable feature in several clinical, research, and educational scenarios. In this work, the integration between 3D Slicer package and Arduino board is introduced, enabling a simple and useful communication between the two software/hardware platforms. The open source extension, programmed in Python language, manages the connection process and offers a communication layer accessible from any point of the medical image suite infrastructure. Deep integration with 3D Slicer code environment is provided and a basic input–output mechanism accessible via GUI is also made available. To test the proposed extension, two exemplary use cases were implemented: (1) INPUT data to 3D Slicer, to navigate on basis of data detected by a distance sensor connected to the board, and (2) OUTPUT data from 3D Slicer, to control a servomotor on the basis of data computed through image process procedures. Both goals were achieved and quasi-real-time control was obtained without any lag or freeze, thus boosting the integration between 3D Slicer and Arduino. This integration can be easily obtained through the execution of few lines of Python code. In conclusion, SlicerArduino proved to be suitable for fast prototyping, basic input–output interaction, and educational purposes. The extension is not intended for mission-critical clinical tasks.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

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