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multilevel segmentation of Histopathological Images Using Cooccurrence of Tissue Objects

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2012年第6期59卷 1681-1690页

作者： Simsek, Ahmet Cagri Tosun, Akif Burak Aykanat, Cevdet Sokmensuer, Cenk Gunduz-Demir, Cigdem Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkey Hacettepe Univ Dept Pathol Sch Med TR-06100 Ankara Turkey

This paper presents a new approach for unsupervised segmentation of histopathological tissue images. This approach has two main contributions. First, it introduces a new set of high-level texture features to represent the prior knowledge of spatial organization of the tissue components. These texture features are defined on the tissue components, which are approximately represented by tissue objects, and quantify the frequency of two component types being cooccurred in a particular spatial relationship. As they are defined on components, rather than on image pixels, these object cooccurrence features are expected to be less vulnerable to noise and variations that are typically observed at the pixel level of tissue images. Second, it proposes to obtain multiple segmentations by multilevel partitioning of a graph constructed on the tissue objects and combine them by an ensemble function. This multilevel graph partitioning algorithm introduces randomization in graph construction and refinements in its multilevel scheme to increase diversity of individual segmentations, and thus, improve the final result. The experiments on 200 colon tissue images reveal that the proposed approach-the object cooccurrence features together with the multilevel segmentation algorithm-is effective to obtain high-quality results. The experiments also show that it improves the segmentation results compared to the previous approaches.

关键词： Histopathological image analysis image segmentation multilevel segmentation segmentation ensemble texture

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multilevel segmentation algorithm for agricultural parcel extraction from a semantic boundary

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第3期44卷 1045-1068页

作者： Wu, Wei Chen, Tingting Yang, Haiping He, Zhiyu Chen, Yuanyuan Wu, Ning Zhejiang Univ Technol Coll Comp Sci Technol Hangzhou Peoples R China Southeast Digital Econ Inst Res Ctr Digital Spatial Technol Quzhou Peoples R China Zhejiang Univ Technol Coll Comp Sci Technol 288 Liuhe Rd Hangzhou 310023 Zhejiang Peoples R China

Parcels are the basic unit of crop planting and management. Therefore, parcelwise farmland data become the fundamental basis for precision agriculture applications, and the extraction of parcels from high-resolution remote sensing images is of great importance. The deep learning-based edge detection methods have achieved superior performance, but these methods output edge intensity maps with pixel values from 0 to 255 in raster format. Vectorization, which transforms the rasterized data into vectors, is an important post-processing procedures. In this process, segmentation and thinning are two key steps for deriving the one-pixel-wide binary edge, however, the traditional method suffers deviation from the actual edge and the unclosed edge. To address these problems, based on the hypothesis that the larger the edge intensity value is, the greater the likelihood that the pixel is on or near the boundary, we developed a multilevel segmentation method for agricultural parcel extraction from a semantic boundary, which prioritizes using the pixels with high intensity to ensure that the extracted boundaries adhere closely to the actual boundaries and the pixels with low intensity connect the unclosed boundaries, thus simultaneously improving the fidelity and completeness of boundaries. We selected images acquired in Hangzhou Bay and Denmark to test our method, and the result demonstrates that our method can accurately extract agricultural parcels. Compared with the single threshold segmentation method, our method shows higher boundary fidelity and completeness. Compared with the state-of-the-art method, our method achieves competitive performance in traditional metrics but outperforms edge preservation and one-to-one correspondence between the extracted parcel and actual parcel.

关键词： Agricultural parcel extraction edge detection multilevel segmentation one-pixel-wide binary edge

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multilevel segmentation for automatic detection of malignant masses in digital mammograms based on threshold comparison 6

Multilevel segmentation for automatic detection of malignant...

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IEEE Latin American Conference on Computational Intelligence (LA-CCI)

作者： Rodriguez-Esparza, Erick Zanella-Calzada, Laura A. Oliva, Diego Hinojosa, Salvador Perez-Cisneros, Marco Univ Guadalajara CUCEI Div Elect & Computac Guadalajara Jalisco Mexico Univ Autonoma Zacatecas Unidad Acad Ingn Elect Zacatecas Zacatecas Mexico Univ Complutense Madrid Dpto Ingn Software & Inteligencia Artificial Madrid Spain

ISBN: (纸本)9781728156668

Digital image processing implies a fundamental process in the field of medicine, since it supports the improvement of images facilitating their interpretation for the specialists. A technique that has had great relevance is image segmentation, applied to mammography images, in order to improve the diagnosis of breast cancer disease. In this work it is proposed the implementation of a new multilevel segmentation approach based on the minimum cross-entropy threshold - Harris Hawks Optimization (MCET-HHO) metaheuristic algorithm, where five different levels are used in order to compare the behavior of the thresholds applied in mammograms with presence of cancer by allowing the identification of malignant tumors. According to the results, by including four levels in the segmentation of the image, it is possible to identify with a significantly high precision the region of interest (ROI) where the tumor is located, obtaining a similarity of 0.902 with the ROI identified by the specialist. Therefore, it is concluded that the implementation of the MCET-HHO algorithm for multilevel segmentation of mammograms allows to determine the ROI that contains malignant masses, presenting a preliminary support tool for the diagnosis of breast cancer.

关键词： Digital Image Processing Mammograms multilevel segmentation Metaheuristic Algorithm MCET-HHO algorithm

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Retinal fundus vasculature multilevel segmentation using whale optimization algorithm

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SIGNAL IMAGE AND VIDEO PROCESSING 2018年第2期12卷 263-270页

作者： Hassan, Gehad Hassanien, Aboul Ella Fayoum Univ Fac Comp & Informat Faiyum Egypt Cairo Univ Fac Comp & Informat Giza Egypt

The aim was to present a novel automated approach for extracting the vasculature of retinal fundus images. The proposed vasculature extraction method on retinal fundus images consists of two phases: preprocessing phase and segmentation phase. In the first phase, brightness enhancement is applied for the retinal fundus images. For the vessel segmentation phase, a hybrid model of multi-level thresholding along with whale optimization algorithm (WOA) is performed. WOA is used to improve the segmentation accuracy through finding the n-1 optimal n-level threshold on the fundus image. To evaluate the accuracy, sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curve analysis measurements are used. The proposed approach achieved an overall accuracy of 97.8%, sensitivity of 88.9%, and specificity of 98.7% for the identification of retinal blood vessels by using a dataset that was collected from Bostan diagnostic center in Fayoum city. The area under the ROC curve reached a value of 0.967. Automated identification of retinal blood vessels based on whale algorithm seems highly successful through a comprehensive optimization process of operational parameters.

关键词： Whale optimization algorithm Vessel segmentation Swarm optimization multilevel segmentation Threshold

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Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2018年第4期30卷 449-461页

作者： Sambandam, Rakoth Kandan Jayaraman, Sasikala Annamalai Univ Dept Comp Sci & Engn Annamalainagar Tamil Nadu India

Dragonfly optimization (DFO) is a population based meta-heuristic optimization algorithm that simulates the static and dynamic swarming behaviors of dragonflies. The static swarm comprising less number of dragonflies in a small area for hunting preys, while the dynamic swarm with a large number of dragonflies migrates over long distances;and they represent the exploration and exploitation phases of the DFO. This paper introduces a self adaptive scheme for tuning the DFO parameters and suggests a methodology involving self-adaptive DFO (SADFO) for performing multilevel segmentation of digital images. The multilevel segmentation problem is formulated as an optimization problem and solved using the SADFO. The method optimizes the threshold values through effectively exploring the solution space in obtaining the global best solution. The results of real life and medical images illustrate the performance of the suggested method. (C) 2016 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： Meta-heuristic algorithms Dragonfly optimization multilevel segmentation

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A HYBRID BUILDING EXTRACTION ALGORITHM BASED ON multilevel segmentation AND BOUNDARY SIMPLIFICATION

A HYBRID BUILDING EXTRACTION ALGORITHM BASED ON MULTILEVEL S...

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作者： OYA YARKINOLU GUCUK MIDDLE EAST TECHNICAL UNIVERSITY

学位级别：博士

Building footprints have an important role in urban planning, infrastructure de- velopment, climate studies and disaster management. The precise knowledge of buildings serves as a primary source for interpreting complex urban charac- teristics and also helps decision makers with more realistic urban management. In this thesis, the building boundary extraction including a hybrid approach is proposed. Firstly, normalized Digital Surface Model (nDSM) of the study area is generated from aerial Light Detection and Ranging (LiDAR) data. During the extraction methodology, multi-leveled segmentation is applied over nDSM data to detect possible building areas of di? ering sizes. Segmented regions of the? rst level are removed from nDSM and proceed to segmentation process until no regions exist. Finally accepted building footprint polygons are sub- jected to boundary extraction and line simpli? cation steps to obtain smooth edges. Furthermore, proposed method results are compared with the results of Otsu algorithm and multi-threshold segmentation functionality of eCognition software. The building extraction accuracy of the proposed method is evaluated by considering pixel-based metrics. Finally, for selected 14 datasets the segmen- tation accuracy of the proposed method have better, detection rate and overall accuracy for 8 datasets. However, for 3 datasets accuracy measures of results are slightly di? erent. For remaining 3 datasets Otsu algorithm gives better results.

关键词： nDSM multilevel segmentation building footprint extraction

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multilevel Image segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2014年第5期52卷 2382-2394页

作者： Ghamisi, Pedram Couceiro, Micael S. Martins, Fernando M. L. Benediktsson, Jon Atli Univ Iceland Fac Elect & Comp Engn IS-101 Reykjavik Iceland Univ Coimbra Inst Syst & Robot DEEC P-3030290 Coimbra Portugal RoboCorp Engn Inst Coimbra Polytech Inst Coimbra P-3030199 Coimbra Portugal Coimbra Coll Educ Inst Telecomunicacoes P-6201001 Coimbra Portugal

Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.

关键词： Classification image processing multilevel segmentation swarm optimization

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Efficient multilevel brain tumor segmentation with integrated Bayesian model classification

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2008年第5期27卷 629-640页

作者： Corso, Jason J. Sharon, Eitan Dube, Shishir El-Saden, Suzie Sinha, Usha Yuille, Alan Univ Calif Los Angeles Dept Radiol Sci Los Angeles CA 90095 USA Technion Israel Inst Technol Dept Elect Engn IL-32000 Haifa Israel Univ Calif Los Angeles Dept Biomed Engn Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Radiol Sci Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Psychol Los Angeles CA 90095 USA

We present a new method for automatic segmentation of heterogeneous image data that takes a step toward bridging the gap between bottom-up affinity-based segmentation methods and top-down generative model based approaches. The main contribution of the paper is a Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free. We integrate the resulting model-aware affinities into the multilevel segmentation by weighted aggregation algorithm, and apply the technique to the task of detecting and segmenting brain tumor and edema in multichannel magnetic resonance (MR) volumes. The computationally efficient method runs orders of magnitude faster than current state-of-the-art techniques giving comparable or improved results. Our quantitative results indicate the benefit of incorporating model-aware affinities into the segmentation process for the difficult case of glioblastoma multi-forme brain tumor.

关键词： Bayesian affinity brain tumor glioblastoma multiforme multilevel segmentation normalized cuts

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Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding

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SOFT COMPUTING 2022年第5期26卷 2587-2623页

作者： Ortega-Sanchez, Noe Rodriguez-Esparza, Erick Oliva, Diego Perez-Cisneros, Marco Mohamed, Ali Wagdy Dhiman, Gaurav Hernandez-Montelongo, Rosaura Univ Guadalajara Div Elect & Comp CUCEI Av Revoluc 1500 Guadalajara Jalisco Mexico Univ Deusto Fac Engn DeustoTech Av Univ 24 Bilbao 48007 Spain Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk Russia Cairo Univ Fac Grad Studies Stat Res Operat Res Dept Giza 12613 Egypt Amer Univ Cairo Sch Sci & Engn Dept Math & Actuarial Sci Cairo Egypt Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India

Identifying the defects in apples is commonly done with visual examination techniques. However, it is a slow and laborious process. Image processing techniques have begun to be used to help and make the diagnosis of fruit diseases more efficient. In image processing systems, the segmentation of regions in the scenes is a crucial step. Specifically for images from apples, disease segmentation is a complicated task due to the different elements that affect the acquisition of the images. In addition, apple diseases also have features that need to be segmented. In this work, an efficient approach that uses the Gaining-sharing Knowledge-based (GSK) algorithm is proposed to optimize the minimum cross-entropy thresholding (MCET) for the segmentation of apple images highlighting the diseases defects. The proposed MCET-GSK has been tested for experimental purposes over different images and compared with various metaheuristics. The experiments were conducted to provide evidence of the GSK's optimization capabilities by performing the Wilcoxon test and applying a set of metrics to verify the quality of the segmented images. The experimental results validate the performance of the MCET-GSK in the segmentation of apple images by adequately separating the regions with damage produced by a disease. The quality of the segmentation is superior compared with other similar approaches.

关键词： Apple diseases identification multilevel segmentation Gaining Sharing Knowledge based (GSK) Apple defects Metaheuristics

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Automatic Building Rooftop Extraction From Aerial Images via Hierarchical RGB-D Priors

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2018年第12期56卷 7369-7387页

作者： Xu, Shibiao Pan, Xingjia Li, Er Wu, Baoyuan Bu, Shuhui Dong, Weiming Xiang, Shiming Zhang, Xiaopeng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Tencent AI Lab Shenzhen 518000 Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

Accurate building rooftop extraction from high-resolution aerial images is of crucial importance in a wide range of applications. Owing to the varying appearance and large-scale range of scene objects, especially for building rooftops in different scales and heights, single-scale or individual prior-based extraction technique is insufficient in pursuing efficient, generic, and accurate extraction results. The trend toward integrating multiscale or several cue techniques appears to be the best way;thus, such integration is the focus of this paper. We first propose a novel salient rooftop detector integrating four correlative RGB-D priors (depth cue, uniqueness prior, shape prior, and transition surface prior) for improved rooftop extraction to address the preceding complex issues mentioned. Then, these correlative cues are computed from image layers created by our multilevel segmentation and further fused into the state-of-the-art high-order conditional random field (CRF) framework to locate the rooftop. Finally, an iterative optimization strategy is applied for high-quality solving, which can robustly handle varying appearance of building rooftops. Performance evaluations in the SZTAKI-INRIA benchmark data sets show that our method outperforms the traditional color-based algorithm and the original high-order CRF algorithm and its variants. The proposed algorithm is also evaluated and found to produce consistently satisfactory results for various large-scale, real-world data sets.

关键词： High-order conditional random field (CRF) multilevel segmentation RGB-D priors rooftop extraction

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