scholarly journals Graph-based unsupervised segmentation algorithm for cultured neuronal networks' structure characterization and modeling

2014 ◽  
Vol 87 (6) ◽  
pp. 513-523 ◽  
Author(s):  
Daniel de Santos-Sierra ◽  
Irene Sendiña-Nadal ◽  
Inmaculada Leyva ◽  
Juan A. Almendral ◽  
Amir Ayali ◽  
...  
Keyword(s):  
Neuronal Networks ◽  
Segmentation Algorithm ◽  
Structure Characterization ◽  
Unsupervised Segmentation ◽  
Networks Structure

Unsupervised segmentation of wear particle’s image using local texture feature

2018 ◽  
Vol 70 (9) ◽  
pp. 1601-1607 ◽  
Author(s):  
Hong Liu ◽  
Haijun Wei ◽  
Haibo Xie ◽  
Lidui Wei ◽  
Jingming Li
Keyword(s):  
Texture Feature ◽  
Segmentation Algorithm ◽  
Great Promise ◽  
Identification System ◽  
Wear Particles ◽  
Small Particles ◽  
Unsupervised Segmentation ◽  
Edge Image ◽  
Content Type ◽  
Local Texture

Purpose The possibility of using a pattern recognition system for wear particle analysis without the need of a human expert holds great promise in the condition monitoring industry. Auto-segmentation of their images is a key to effective on-line monitoring system. Therefore, an unsupervised segmentation algorithm is required. The purpose of this paper is to present a novel approach based on a local color-texture feature. An algorithm is specially designed for segmentation of wear particles’ thin section images. Design/methodology/approach The wear particles were generated by three kinds of tribo-tests. Pin-on-disk test and pin-on-plate test were done to generate sliding wear particles, including severe sliding ones; four-ball test was done to generate fatigue particles. Then an algorithm base on local texture property is raised, it includes two steps, first, color quantization reduces the total quantity of the colors without missing too much of the detail; second, edge image is calculated and by using a region grow technique, the image can be divided into different regions. Parameters are tested, and a criterion is designed to judge the performances. Findings Parameters have been tested; the scale chosen has significant influence on edge image calculation and seeds generation. Different size of windows should be applied to varies particles. Compared with traditional thresholding method along with edge detector, the proposed algorithm showed promising result. It offers a relatively higher accuracy and can be used on color image instead of gray image with little computing complexity. A conclusion can be drawn that the present method is suited for wear particles’ image segmentation and can be put into practical use in wear particles’ identification system. Research limitations/implications One major problem is when small particles with similar texture are attached, the algorithm will not take them as two but as one big particle. The other problem is when dealing with thin particles, mainly abrasive particles, the algorithm usually takes it as a single line instead of an area. These problems might be solved by introducing a smaller scale of 9 × 9 window or by making use of some edge enhance technique. In this way, the subtle edges between small particles or thin particles might be detected. But the effectiveness of a scale this small shall be tested. One can also magnify the original picture to double or even triple its size, but it will dramatically increase the calculating time. Originality/value A new unsupervised segmentation algorithm is proposed. Using the property of the edge image, we can get target out of its background, automatically. A rather complete research is done. The method is not only introduced but also completely tested. The authors examined parameters and found the best set of parameters for different kinds of wear particles. To ensure that the proposed method can work on images under different condition, three kinds of tribology tests have been carried out to simulate different wears. A criterion is designed so that the performances can be compared quantitatively which is quite valuable.

scholarly journals Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

2013 ◽  
Author(s):  
Qaiser Mahmood ◽  
Mohammad Alipoor ◽  
Artur Chodorowski ◽  
Andrew Mehnert1 ◽  
Mikael Persson
Keyword(s):  
Mean Shift ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Brain Segmentation ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
The Brain ◽  
Mr Brain ◽  
Dice Index

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the adaptive mean shift wherein the adaptive bandwidth of the kernel for each feature point is estimated using our proposed Bayesian approach [1]. BAMS is designed to segment the brain into three tissues; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The performance of the algorithm is evaluated relative to the manual segmentation (ground truth). The results of our proposed algorithm show the average Dice index 0.8377±0.036 for the WM, 0.7637±0.038 for the GM and 0.6835 ±0.023 for the CSF.

Liver Cancer Detection System Based on the Analysis of Digitized Color Images of Tissue Samples Obtained Using Needle Biopsy

2002 ◽  
Vol 1 (2) ◽  
pp. 130-138 ◽  
Author(s):  
Mohamed Sammouda ◽  
Rachid Sammouda ◽  
Noboru Niki ◽  
Kiyoshi Mukai
Keyword(s):  
Liver Cancer ◽  
Needle Biopsy ◽  
Detection System ◽  
Color Space ◽  
Hopfield Neural Network ◽  
Color Images ◽  
Segmentation Algorithm ◽  
Space Representation ◽  
Unsupervised Segmentation ◽  
Tissue Samples

In this article, the authors propose a method for automatic diagnosis of liver cancer based on analysis of digitized color images of liver tissue obtained by needle biopsy. The approach is a combination of an unsupervised segmentation algorithm, using a modified artificial Hopfield neural network (HNN), and an analysis algorithm based on image quantization. The segmentation algorithm is superior to HNN in the sense that it converges to a nearby global minimum rather than a local one in a prespecified time. Furthermore, as the segmentation of color images does not only depend on the segmentation algorithm but also on the color space representation, and in order to choose the best segmentation result, segmentation was performed with HNN and using components of the raw image with respect to each of the RGB, HLS, and HSV color spaces. Then, the segmented image was labeled based on chromaticity features and histogram analysis of the RGB color space components of the raw image. The image regions were then classified into normal and cancerous using diagnostic rules formulated based on those used by experienced pathologists in the clinic. The proposed method provides quantitative satisfactory results in diagnosing a liver pathological image set of 17 cases.

scholarly journals Transition state clustering: Unsupervised surgical trajectory segmentation for robot learning

2017 ◽  
Vol 36 (13-14) ◽  
pp. 1595-1618 ◽  
Author(s):  
Sanjay Krishnan ◽  
Animesh Garg ◽  
Sachin Patil ◽  
Colin Lea ◽  
Gregory Hager ◽  
...  
Keyword(s):  
Transition State ◽  
Dirichlet Process ◽  
Piecewise Linear ◽  
Gaussian Mixture ◽  
Robot Learning ◽  
Segmentation Algorithm ◽  
Linear Segment ◽  
Unsupervised Segmentation ◽  
Segmentation Algorithms ◽  
Trajectory Segmentation

Demonstration trajectories collected from a supervisor in teleoperation are widely used for robot learning, and temporally segmenting the trajectories into shorter, less-variable segments can improve the efficiency and reliability of learning algorithms. Trajectory segmentation algorithms can be sensitive to noise, spurious motions, and temporal variation. We present a new unsupervised segmentation algorithm, transition state clustering (TSC), which leverages repeated demonstrations of a task by clustering segment endpoints across demonstrations. TSC complements any motion-based segmentation algorithm by identifying candidate transitions, clustering them by kinematic similarity, and then correlating the kinematic clusters with available sensory and temporal features. TSC uses a hierarchical Dirichlet process Gaussian mixture model to avoid selecting the number of segments a priori. We present simulated results to suggest that TSC significantly reduces the number of false-positive segments in dynamical systems observed with noise as compared with seven probabilistic and non-probabilistic segmentation algorithms. We additionally compare algorithms that use piecewise linear segment models, and find that TSC recovers segments of a generated piecewise linear trajectory with greater accuracy in the presence of process and observation noise. At the maximum noise level, TSC recovers the ground truth 49% more accurately than alternatives. Furthermore, TSC runs 100× faster than the next most accurate alternative autoregressive models, which require expensive Markov chain Monte Carlo (MCMC)-based inference. We also evaluated TSC on 67 recordings of surgical needle passing and suturing. We supplemented the kinematic recordings with manually annotated visual features that denote grasp and penetration conditions. On this dataset, TSC finds 83% of needle passing transitions and 73% of the suturing transitions annotated by human experts.

AN UNSUPERVISED COLOR-TEXTURE SEGMENTATION USING TWO-STAGE FUZZY c-MEANS ALGORITHM

2014 ◽  
Vol 28 (02) ◽  
pp. 1455002 ◽  
Author(s):  
SHAOPING XU ◽  
LINGYAN HU ◽  
CHUNQUAN LI ◽  
XIAOHUI YANG ◽  
XIAOPING P. LIU
Keyword(s):  
Computer Vision ◽  
Multiple Scales ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Segmentation Algorithm ◽  
Unsupervised Segmentation ◽  
Two Stage ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Occurrence Matrix

Unsupervised image segmentation is a fundamental but challenging problem in computer vision. In this paper, we propose a novel unsupervised segmentation algorithm, which could find diverse applications in pattern recognition, particularly in computer vision. The algorithm, named Two-stage Fuzzy c-means Hybrid Approach (TFHA), adaptively clusters image pixels according to their multichannel Gabor responses taken at multiple scales and orientations. In the first stage, the fuzzy c-means (FCM) algorithm is applied for intelligent estimation of centroid number and initialization of cluster centroids, which endows the novel segmentation algorithm with adaptivity. To improve the efficiency of the algorithm, we utilize the Gray Level Co-occurrence Matrix (GLCM) feature extracted at the hyperpixel level instead of the pixel level to estimate centroid number and hyperpixel-cluster memberships, which are used as initialization parameters of the following main clustering stage to reduce the computational cost while keeping the segmentation performance in terms of accuracy close to original one. Then, in the second stage, the FCM algorithm is utilized again at the pixel level to improve the compactness of the clusters forming final homogeneous regions. To examine the performance of the proposed algorithm, extensive experiments were conducted and experimental results show that the proposed algorithm has a very effective segmentation results and computational behavior, decreases the execution time and increases the quality of segmentation results, compared with the state-of-the-art segmentation methods recently proposed in the literature.

Sign in / Sign up

Export Citation Format

Share Document