Color dependent K-means clustering for color image segmentation of colored medical images

Yadav, Himanshu; Bansal, Prateek; KumarSunkaria, Ramesh

doi:10.1109/ngct.2015.7375241

Cited by 17 publications

(12 citation statements)

References 3 publications

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“…Segmentation refers to a process of dividing an image into meaningful regions or objects [27]. The segmentation approach is used to achieve a more accurate result before further processes such as recognition, classification or other identification are performed [28].…”

Section: Edge Detectionmentioning

confidence: 99%

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Pre-trained classification of scalp conditions using image processing

Ibrahim

Azmy

Mangshor

et al. 2020

IJEECS

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<span>Scalp problems may occur due to the miscellaneous factor, which includes genetics, stress, abuse and hair products. The conventional technique for scalp and hair treatment involves high operational cost and complicated diagnosis. Besides, it is becoming progressively important for the payer to investigate the value of new treatment selection in the management of a specific scalp problem. As they are generally expensive and inconvenient, there is an increasing need for an affordable and convenient way of monitoring scalp conditions. Thus, this paper presents a study of pre-trained classification of scalp conditions using image processing techniques. Initially, the scalp image went through the pre-processing such as image enhancement and greyscale conversion. Next, three features of color, texture, and shape were extracted from each input image, and stored in a Region of Interest (ROI) table. The knowledge of the values of the pre-trained features is used as a reference in the classification process subsequently. A technique of Support Vector Machine (SVM) is employed to classify the three types of scalp conditions which are alopecia areata (AA), dandruff and normal. A total of 120 images of the scalp conditions were tested. The classification of scalp conditions indicated a good performance of 85% accuracy. It is expected that the outcome of this study may automatically classify the scalp condition, and may assist the user on a selection of suitable treatment available.</span>

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Section: Edge Detectionmentioning

confidence: 99%

“…The data is classified according to the statistical pattern. The SVM is also known as a binary classifier, in which data is classified according to the hyperplane [27]. It supports classification and regression functions, and can handle several continuous and categorical variables [39].…”

Section: Scalp Problem Classificationmentioning

confidence: 99%

Pre-trained classification of scalp conditions using image processing

Ibrahim

Azmy

Mangshor

et al. 2020

IJEECS

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“…Himanshu et al, [7] have used neoteric image segmentation technique and a color image is converted into Lab in various computational steps. Then segmentation of nuclei into the separate image is done by recalling L layer.…”

Section: Related Workmentioning

confidence: 99%

Object Detection using K-Means Clustering – A Research

2019

IJRTE

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Clustering is an unsupervised machine learning technique and the task is to group a set of objects in such a way that objects in the same group are more similar to each other than those in other groups. There are different clustering techniques, each with its own advantages and disadvantages. The K-means clustering algorithm is our main focus in this paper. K-means is mostly used when there is a large number of unlabeled data. The difficulties in the path of K-means clustering are a) Different initial points can lead to different final clusters. b) It does not work with clusters of different sizes and densities. c) With the global cluster, it does not work well and is difficult to determine K Value. Our main aim is to try and modify the K-means clustering algorithm to get rid of the above-mentioned drawbacks.

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“…Normalization techniques discussed in the study [1] where the images are classified by their colors using K Means Clustering and JSEG segmentation In this method, the nuclei get segmented as a separate segment. Then it is passed onto the SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hepatocellular Carcinoma Using Machine Learning

Kalinathan¹,

Sivasankaran²,

Jeyasingh³

et al. 2022

Hepatocellular Carcinoma - Challenges and Opportunities of a Multidisciplinary Approach

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Hepatocellular Carcinoma (HCC) proves to be challenging for detection and classification of its stages mainly due to the lack of disparity between cancerous and non cancerous cells. This work focuses on detecting hepatic cancer stages from histopathology data using machine learning techniques. It aims to develop a prototype which helps the pathologists to deliver a report in a quick manner and detect the stage of the cancer cell. Hence we propose a system to identify and classify HCC based on the features obtained by deep learning using pre-trained models such as VGG-16, ResNet-50, DenseNet-121, InceptionV3, InceptionResNet50 and Xception followed by machine learning using support vector machine (SVM) to learn from these features. The accuracy obtained using the system comprised of DenseNet-121 for feature extraction and SVM for classification gives 82% accuracy.

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Color dependent K-means clustering for color image segmentation of colored medical images

Cited by 17 publications

References 3 publications

Pre-trained classification of scalp conditions using image processing

Pre-trained classification of scalp conditions using image processing

Object Detection using K-Means Clustering – A Research

Classification of Hepatocellular Carcinoma Using Machine Learning

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