Medical Image Segmentation Using Improved Mountain Clustering Approach

Verma, Nishchal K.; Gupta, Payal; Agrawal, Pooja; Hanmandlu, M.; Vasikarla, Shantaram; Cui, Yan

doi:10.1109/itng.2009.238

Cited by 9 publications

(3 citation statements)

References 11 publications

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“…In image processing, segmentation of the image can be the solution for this problem and there are several methods for segmentation technique such as edge method [44], threshold method [45], cluster method [46], [47] and neural network-based method [48]. Clustering based method is the most powerful for image segmentation and there were branches of clustering method such as K-means clustering [49][50][51][52] [53], Fuzzy C-means clustering [54], [55], mountain clustering [56] and subtractive clustering method [53]. Generally, clustering is a grouping approach that uses a similarity metric to place comparable things in the same group and dissimilar ones in distinct groupings.…”

Section: Image Processing a K -Means Segmentationmentioning

confidence: 99%

Region Detection Technique Using Image Subtraction and Pixel Expansion Cue for Obstacle Detection System on Small – Sized UAV

Wafi Abdul Aziz,

Faiz Ramli

2024

IJCDS

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This research paper is about method of detection of free region and obstacle region by combining image segmentation and frame subtraction method. The application will be further study to be used by Unmanned Aerial Vehicles (UAVs). This method intends to minimize the weight of UAV by avoiding heavy sensors. Objective of this research is to utilize the pixel expansion of object to find free region. K-means segmentation will be used to separate the interest area from the background. Then, segmented image frame will be subtracted and then divided into several grids and the amount of subtracted pixel that has been set into yellow and black color of each grid will be calculated with respect to distance given. The expansion of pixel will be detected as, the distance between image frame coming closer, number of obstacle pixel will be higher. The application of simple LIDAR that emits single ray to frontal obstacle will initiate the camera to capture image frame to be further analyze. Experiment was carried out in close environment with different cases and total of 100 images has been captured that consist of texture obstacle, texture-less obstacle, and multiple obstacles. The findings showed bearable results as the free region detection is 88.0% for texture obstacle and up to 84.0% of free region were successfully detected for texture-less obstacle. Due to lack of cues and texture in texture-less object, the algorithm had difficulties detecting the center of object that expands in static form.

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Section: Image Processing a K -Means Segmentationmentioning

confidence: 99%

Region Detection Technique Using Image Subtraction and Pixel Expansion Cue for Obstacle Detection System on Small – Sized UAV

Wafi Abdul Aziz,

Faiz Ramli

2024

IJCDS

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“…A hard clustering algorithm puts each pattern in only one cluster and gives the result during run time. A fuzzy clustering method provides each input with a membership degree based on how close it is to more than one cluster [6,7]. The aim is to present a new way to solve the problem of separating parts of a medical image by using improved clustering techniques based on image processing and data clustering techniques that already exist.…”

Section: Introductionmentioning

confidence: 99%

Clustering of Medical Images Using Multiwavelet Transform and K-Means Algorithms

Al-Jawher¹,

SHABAN²

2022

J.port.sci.res.

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Imaging data and biological records are both included in the field of medical informatics. Medical image clustering is an important area of research that is getting increased interest in both academia and the health professions. It addresses the issues of medical diagnosis, analysis, and education. Several medical imaging methods and applications based on data mining have been made and tested to deal with these problems. This paper looks at how image classification techniques diagnose diseases in the human body. It concerns the imaging modalities, dataset, and the pros and cons of each method. An optimal clustering technique of medical images using multiwavelet transforms proposed that combines the multiwavelet transform filterbanks with the k-means clustering algorithm to improve the performance and get a clinically meaningful clustering shape. In comparison with other methods of clustering, it was shown that this method has a much higher cluster classification than those published before. A user-friendly Matlab program has been constructed to test and get the results of the proposed algorithms

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“…Neste modelo, os autores customizaram o algoritmo de ordenação de frutos, por apresentar relativa precisão na detecção de bordas das formas geométricas dos frutos, sendo desnecessária qualquer etapa de pré-processamento, mesmo para imagens com ruídos e desfocadas Si (SI et al, 2009). e Lee(LEE et al, 2009) propuseram um trabalho de reconhecimento de maçãs baseado no método analítico por clustering (algoritmo k-means) (e o valor médio, ao passo que para a caracterização de texturas foram escolhidos o desvio padrão e entropia em imagens com níveis de cinza para a posterior extração de características VERMA et al, 2009;ZHANG et al, 2009)…”

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Técnicas de visão computacional aplicadas ao reconhecimento de cenas naturais e locomoção autônoma em robôs agrícolas móveis

Lulio¹

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Medical Image Segmentation Using Improved Mountain Clustering Approach

Cited by 9 publications

References 11 publications

Region Detection Technique Using Image Subtraction and Pixel Expansion Cue for Obstacle Detection System on Small – Sized UAV

Region Detection Technique Using Image Subtraction and Pixel Expansion Cue for Obstacle Detection System on Small – Sized UAV

Clustering of Medical Images Using Multiwavelet Transform and K-Means Algorithms

Técnicas de visão computacional aplicadas ao reconhecimento de cenas naturais e locomoção autônoma em robôs agrícolas móveis

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