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A colour image segmentation method and its application to medical images

SIGNAL IMAGE AND VIDEO PROCESSING 2024年第2期18卷 1635-1648页

作者： Halim, Abdul Kumar, B. V. Rathish Niranjan, Ajay Nigam, Aditya Schneider, Walter Ahuja, Chirag K. Pathak, Sudhir K. King Abdullah Univ Sci & Technol Thuwal Saudi Arabia Hari Singh Coll Dept Math Munger 811213 Bihar India IIT Dept Math & Stat Kanpur 208016 UP India Univ Pittsburgh Sch Med Neurol Surg Pittsburgh PA USA IIT Mandi Sch Comp & Elect Engn Mandi India Univ Pittsburgh Learning Res & Dev Ctr Pittsburgh PA USA PGIMER Chandigarh Dept Radio Diag & Imaging Chandigarh India

In this paper, we propose a segmentation model using an anisotropic multi-well potential-based nonlinear transient PDE for colour images. A channel-wise greyscale classification approach is devised for colour image segmentation. The time evolution of the PDE model is carried out by the implicit-explicit convexity splitting approach. Further, we consider the fractional version of the time-discretised model by replacing the Laplacian with its fractional counterpart. The spatial terms are approximated by the Fourier basis under the pseudo-spectral method. The convergence and the stability of the numerical scheme are elaborated. Both models (fractional and non-fractional) are tested on some synthetic images and few real-world standard test images. The results on synthetic images are compared with those from the literature using Dice similarity index, Jaccard similarity index and BF score. Later the method is successfully applied on several medical images to classify the same.

关键词： colour segmentation Multi-well potential Nonlinear PDE Fractional PDEs Medical imaging

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Linear colour segmentation Revisited 11

Linear Colour Segmentation Revisited

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11th International Conference on Machine Vision (ICMV)

作者： Smagina, Anna Bozhkova, Valentina Gladilin, Sergey Nikolaev, Dmitry Russian Acad Sci Kharkevich Inst Inst Informat Transmiss Problems Moscow Russia

ISBN: (数字)9781510627499

ISBN: (纸本)9781510627499

In this work we discuss the known algorithms for linear colour segmentation based on a physical approach and propose a new modification of segmentation algorithm. This algorithm is based on a region adjacency graph framework without a pre-segmentation stage. Proposed edge weight functions are defined from linear image model with normal noise. The colour space projective transform is introduced as a novel pre-processing technique for better handling of shadow and highlight areas. The resulting algorithm is tested on a benchmark dataset consisting of the images of 19 natural scenes selected from the Barnard's DXC-930 SFU dataset and 12 natural scene images newly published for common use. The dataset is provided with pixel-by-pixel ground truth colour segmentation for every image. Using this dataset, we show that the proposed algorithm modifications lead to qualitative advantages over other model-based segmentation algorithms, and also show the positive effect of each proposed modification. The source code and datasets for this work are available for free access at http://***/visillect/segmentation.

关键词： colour segmentation colour space colour homography clusterisation

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Automatic Outcome in Manual Dexterity Assessment Using colour segmentation and Nearest Neighbour Classifier

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SENSORS 2018年第9期18卷 2876-2876页

作者： Ona, Edwin Daniel Sanchez-Herrera, Patricia Cuesta-Gomez, Alicia Martinez, Santiago Jardon, Alberto Balaguer, Carlos Univ Carlos III Madrid Dept Syst Engn & Automat Avda Univ 30 Leganes 28911 Spain Rey Juan Carlos Univ Dept Phys Therapy Occupat Therapy Rehabil & Phys Avda Atenas S-N Alcorcon 28922 Spain

Objective assessment of motor function is an important component to evaluating the effectiveness of a rehabilitation process. Such assessments are carried out by clinicians using traditional tests and scales. The Box and Blocks Test (BBT) is one such scale, focusing on manual dexterity evaluation. The score is the maximum number of cubes that a person is able to displace during a time window. In a previous paper, an automated version of the Box and Blocks Test using a Microsoft Kinect sensor was presented, and referred to as the Automated Box and Blocks Test (ABBT). In this paper, the feasibility of ABBT as an automated tool for manual dexterity assessment is discussed. An algorithm, based on image segmentation in CIELab colour space and the Nearest Neighbour (NN) rule, was developed to improve the reliability of automatic cube counting. A pilot study was conducted to assess the hand motor function in people with Parkinson's disease (PD). Three functional assessments were carried out. The success rate in automatic cube counting was studied by comparing the manual (BBT) and the automatic (ABBT) methods. The additional information provided by the ABBT was analysed to discuss its clinical significance. The results show a high correlation between manual (BBT) and automatic (ABBT) scoring. The lowest average success rate in cube counting for ABBT was 92%. Additionally, the ABBT acquires extra information from the cubes' displacement, such as the average velocity and the time instants in which the cube was detected. The analysis of this information can be related to indicators of health status (coordination and dexterity). The results showed that the ABBT is a useful tool for automating the assessment of unilateral gross manual dexterity, and provides additional information about the user's performance.

关键词： colour segmentation CIELab automatic counting NN-based classifier manual dexterity assessment neurological rehabilitation

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Chroma based colour Enhancement for Improved colour segmentation 9

Chroma based Colour Enhancement for Improved Colour Segmenta...

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2015 9th International Conference on Sensing Technology (ICST)

作者： Su, Feng Fang, Gu Univ Western Sydney Sch Comp Engn & Math Locked Bag 1797 Penrith NSW 2751 Australia

ISBN: (纸本)9781479963140

There are many applications rely on the use of colour segmentation in image processing. The major challenge with colour segmentation is that there is no unique solution as each person perceives and segments the colours in images differently. The segmentation results are also heavily influenced by environment factors such as lighting conditions and noise levels. In addition, existing methods tend to focus on the segmentation stage alone and employ little to none for pre-processing. Thus it can be a challenge task to develop an efficient colour segmentation method. In this paper, a novel colour enhancement method is introduced to boost the colour saliency of the critical regions and to improve the consistency of segmentation results by maximizing chroma while preserving hue. Tests in various common colour models using Euclidean distance segmentation method have shown that images pre-processed with the novel colour enhancement method produced much more accurate and reliable colour segmentation results than images without the enhancement or enhancement using existing methods.

关键词： colour enhancement colour segmentation image pre-processing

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Chroma based colour Enhancement for Improved colour segmentation

Chroma based Colour Enhancement for Improved Colour Segmenta...

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International Conference on Sensing Technology

作者： Feng Su Gu Fang School of Computing Engineering and Mathematics University of Western Sydney

ISBN: (纸本)9781479963157

关键词： colour enhancement colour segmentation Image pre-processing

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Leveraging colour segmentation for upper-body detection

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PATTERN RECOGNITION 2014年第6期47卷 2222-2230页

作者： Duffner, Stefan Odobez, Jean-Marc Idiap Res Inst CH-1920 Martigny Switzerland

This paper presents an upper-body detection algorithm that extends classical shape-based detectors through the use of additional semantic colour segmentation cues. More precisely, candidate upper-body image patches produced by a base detector are soft-segmented using a multi-class probabilistic colour segmentation algorithm that leverages spatial as well as colour prior distributions for different semantic object regions (skin, hair, clothing, background). These multi-class soft segmentation maps are then classified as true or false upper-bodies. By further fusing the score of this latter classifier with the base detection score, the method shows a performance improvement on three different public datasets and using two different upper-body base detectors, demonstrating the complementarity of the contextual semantic colour segmentation and the base detector. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Detection colour segmentation Upper-body

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Automated variety trial plot growth and flowering detection for maize and soybean using machine vision

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 194卷 106727-106727页

作者： McCarthy, Alison Raine, Steven Univ Southern Queensland Ctr Agr Engn Inst Adv Engn & Space Sci West St Toowoomba Qld 4350 Australia Univ Southern Queensland Inst Agr & Environm West St Toowoomba Qld 4350 Australia

Hundreds of crop variety trial sites are operated across Australia with up to 100 small plots (20 m(2)) that require manual, labour-intensive monitoring for emergence, height, canopy cover and flowering status. Machine vision systems can reduce labour in monitoring, and infield fixed cameras are most suitable to provide low-cost continuous sensing without requiring labour of travelling to sites. Height is commonly detected using stereo cameras;however, a low-cost single camera system is preferable for broad scale use at variety trial sites. Existing low-cost fixed camera systems assess multiple plots in the image's field of view from a fixed mask that needs to be manually updated as the crop grows. Perspective transformation could be applied automatically to identify plot locations in the image. Existing systems also analyse multiple images independently and filter results to remove impacts from lighting variations. An alternative approach is to only analyse images in the same lighting conditions each day to reduce the need for filtering. In addition, a series of daily images can be used to track multiple leaf positions to monitor plant growth. A machine vision system has been developed that combines these technologies to track plant height from a series of daily images selected in the same lighting conditions, in combination with perspective transformation, in multiple plots in the camera's field of view. Emergence, canopy cover and flowering status were also identified at the canopy surface in each plot using colour segmentation and/or shape analysis. The algorithms were evaluated on 5 and 10 m towers monitoring randomised trials of 16 maize and soybean plots and detected maize flowering date within one day, soybean height (RMSE = 18.38 cm;R-2 = 0.880), maize height (RMSE = 47.73 cm;R-2 = 0.838), soybean canopy cover (RMSE = 22.14%;R-2 = 0.818) and maize canopy cover (RMSE = 14.01%;R-2 = 0.750). The larger error in maize height detection was due to the flowers bei

关键词： Oblique imagery Smartphone Perspective transformation Feature tracking colour segmentation

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Mutation mapping of a variegated EMS tomato reveals an FtsH-like protein precursor potentially causing patches of four phenotype classes in the leaves with distinctive internal morphology

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BMC PLANT BIOLOGY 2024年第1期24卷 265页

作者： Dechkrong, Punyavee Srima, Sornsawan Sukkhaeng, Siriphan Utkhao, Winai Thanomchat, Piyanan de Jong, Hans Tongyoo, Pumipat Kasetsart Univ Cent Lab & Greenhouse Complex Res & Acad Serv Ctr Fac Agr Kamphaeng Saen Kamphaeng Saen Campus Kamphaeng Saen 73140 Nakhon Pathom Thailand Ctr Excellence Agr Biotechnol AG BIO MHESRI Bangkok 10900 Thailand Kasetsart Univ Ctr Agr Biotechnol Kamphaeng Saen Campus Nakhon Pathom 73140 Thailand Kasetsart Univ Kasetsart Univ Res & Dev Inst KURDI Sci Equipment & Res Div Bangkok 10900 Thailand Wageningen Univ Lab Genet Plant Sci Grp Droevendaalsesteeg 1 NL-6708 PB Wageningen Netherlands

Background Leaf variegation is an intriguing phenomenon observed in many plant species. However, questions remain on its mechanisms causing patterns of different colours. In this study, we describe a tomato plant detected in an M2 population of EMS mutagenised seeds, showing variegated leaves with sectors of dark green (DG), medium green (MG), light green (LG) hues, and white (WH). Cells and tissues of these classes, along with wild-type tomato plants, were studied by light, fluorescence, and transmission electron microscopy. We also measured chlorophyll a/b and carotene and quantified the variegation patterns with a machine-learning image analysis tool. We compared the genomes of pooled plants with wild-type-like and mutant phenotypes in a segregating F2 population to reveal candidate genes responsible for the variegation. Results A genetic test demonstrated a recessive nuclear mutation caused the variegated phenotype. Cross-sections displayed distinct anatomy of four-leaf phenotypes, suggesting a stepwise mesophyll degradation. DG sectors showed large spongy layers, MG presented intercellular spaces in palisade layers, and LG displayed deformed palisade cells. Electron photomicrographs of those mesophyll cells demonstrated a gradual breakdown of the chloroplasts. Chlorophyll a/b and carotene were proportionally reduced in the sectors with reduced green pigments, whereas white sectors have hardly any of these pigments. The colour segmentation system based on machine-learning image analysis was able to convert leaf variegation patterns into binary images for quantitative measurements. The bulk segregant analysis of pooled wild-type-like and variegated progeny enabled the identification of SNP and InDels via bioinformatic analysis. The mutation mapping bioinformatic pipeline revealed a region with three candidate genes in chromosome 4, of which the FtsH-like protein precursor (LOC100037730) carries an SNP that we consider the causal variegated phenotype mutation. Phy

关键词： Leaf variegation Palisade Mesophyll Cell degradation Chloroplast FtsH colour segmentation

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colour band fusion and region enhancement of spectral image using multivariate histogram

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INTERNATIONAL JOURNAL OF IMAGE AND DATA FUSION 2021年第1期12卷 64-82页

作者： Karmakar, Dhiman Sarkar, Rajib Datta, Madhura Surendranath Coll Dept Comp Sci 24-2 MG Rd Kolkata 700009 W Bengal India Nibedita Polytech Dept Comp Sci & Technol Berhampur W Bengal India Univ Calcutta UGC Human Resource Dev Ctr Kolkata India

Multi-spectral satellite remote sensing imagery have several applications including detection of objects or distinguishing land surface areas based on amount of greenery or water etc. The enhancement of spectral images helps extracting and visualizing spatial and spectral features. This paper identifies some specific regions of interest (RoI) of the earth's surface from the remotely sensed spectral or satellite image. The RoI are extracted and identified as major segments. Trivially, uni-variate histogram thresholding is used for gray images as a tool of segmentation. However, for color images multivariate histogram is effective to get control on color bands. It also helps emphasizing color information for clustering purpose. In this paper, the 2D and 3D histograms are used for clustering pixels in order to extract the RoI. The RGB color bands along with the infrared (IR) band information are used to form the multivariate histogram. Two datasets are used to carry out the experiment. The first one is an artificially designed dataset and the next is Indian Remotely Sensed (IRS-1A) satellite imagery. This paper proves the correctness of the proposed mathematical implication on the artificial dataset and consequently perform the application on LandSat Spectral data. The test result is found to be satisfactory.

关键词： colour band colour segmentation feature fusion multivariate histogram remote sensing spectral images

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Mapping and assessing spatial extent of floods from multitemporal synthetic aperture radar images: a case study on Brahmaputra River in Assam State, India

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第2期27卷 1521-1532页

作者： Surampudi, Samvedya Yarrakula, Kiran Vellore Inst Technol Ctr Disaster Management & Mitigat Vellore Tamil Nadu India

Brahmaputra is one of the perennial rivers in India which causes floods every year in the north-east state of Assam causing hindrance to normal life and damage to crops. The availability of temporal Remote Sensing (RS) data helps to study the periodical changes caused by flood event and its eventual effect on natural environment. Integrating RS and GIS methods paved a way for effective flood mapping over a large spatial extent which helps to assess the damage accurately for mitigation. In the present study, multitemporal Sentinel-1A data is exploited to assess the 2017 flood situation of Brahmaputra River in Assam state. Five data sets that are taken during flood season and one reference data taken during the non-monsoon season are used to estimate the area inundated under floods for the quantification of damage assessment. A visual interpretation map is produced using colour segmentation method by estimating the thresholds from histogram analysis. A new method is developed to identify the optimum value for threshold from statistical distribution of Synthetic Aperture Radar (SAR) data that separates flooded water and non-flooded water. From this method, the range of backscatter values for normal water are identified as - 18 to - 30 dB and the range is identified as - 19 to - 24 dB for flooded water. The results showed that the method is able to separate the flooded and non-flooded region on the microwave data set, and the derived flood extent using this method shows the inundated area of 3873.14 Km(2) on peak flood date for the chosen study area.

关键词： Assam 2017 floods Visual interpretation map colour segmentation Statistical distribution Histogram analysis

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