Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

Devi, Swapna; Singh, Ngangbam Herojit; Laskar, Rabul Hussain

doi:10.9781/ijimai.2020.01.001

Cited by 22 publications

(12 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to their elegance and efficiency, a wide gamut of unsupervised segmentation methods has been developed to date and applied for the analysis of skin lesions. These methods include thresholding [ 17 , 19 ], clustering [ 20 , 21 , 22 , 23 ], region merging [ 8 , 10 , 39 ], and saliency [ 19 , 24 , 25 , 27 , 28 , 29 , 38 ]. More recently, a privacy-preserving segmentation method based on the partially homomorphic permutation ordered binary (POB) number system was proposed for the segmentation of encrypted skin lesions [ 53 ].…”

Section: Related Studiesmentioning

confidence: 99%

“…Moreover, the leakage issue in the process of region growing through the weak boundary is another drawback of region merging methods [ 54 ]. Color cue is an important feature commonly used in clustering methods for skin disease identification [ 55 ] and several color-based clustering methods were proposed over the years for segmenting skin lesions [ 20 , 21 , 22 , 23 ]. However, the automatic identification of the optimum number of clusters in an image is a major challenge experienced by clustering methods.…”

Section: Related Studiesmentioning

confidence: 99%

“…Myriads of image segmentation methods have been proposed in the literature for skin lesions. They include thresholding [ 17 , 19 ], clustering [ 20 , 21 , 22 , 23 ], statistical region merging [ 8 , 10 ], saliency [ 19 , 24 , 25 , 26 , 27 , 28 , 29 ], and deep learning [ 2 , 30 , 31 , 32 , 33 , 34 , 35 ]. Although multifarious image segmentation methods exist in the literature, accurate segmentation of skin lesions is still a challenging open problem because of the heterogeneous properties of dermoscopic images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

View full text Add to dashboard Cite

Despite the recent advances in immune therapies, melanoma remains one of the deadliest and most difficult skin cancers to treat. Literature reports that multifarious driver oncogenes with tumor suppressor genes are responsible for melanoma progression and its complexity can be demonstrated by alterations in expression with signaling cascades. However, a further improvement in the therapeutic outcomes of the disease is highly anticipated with the aid of humanoid assistive technologies that are nowadays touted as a superlative alternative for the clinical diagnosis of diseases. The development of the projected technology-assistive diagnostics will be based on the innovations of medical imaging, artificial intelligence, and humanoid robots. Segmentation of skin lesions in dermoscopic images is an important requisite component of such a breakthrough innovation for an accurate melanoma diagnosis. However, most of the existing segmentation methods tend to perform poorly on dermoscopic images with undesirable heterogeneous properties. Novel image segmentation methods are aimed to address these undesirable heterogeneous properties of skin lesions with the help of image preprocessing methods. Nevertheless, these methods come with the extra cost of computational complexity and their performances are highly dependent on the preprocessing methods used to alleviate the deteriorating effects of the inherent artifacts. The overarching objective of this study is to investigate the effects of image preprocessing on the performance of a saliency segmentation method for skin lesions. The resulting method from the collaboration of color histogram clustering with Otsu thresholding is applied to demonstrate that preprocessing can be abolished in the saliency segmentation of skin lesions in dermoscopic images with heterogeneous properties. The color histogram clustering is used to automatically determine the initial clusters that represent homogenous regions in an input image. Subsequently, a saliency map is computed by agglutinating color contrast, contrast ratio, spatial feature, and central prior to efficiently detect regions of skin lesions in dermoscopic images. The final stage of the segmentation process is accomplished by applying Otsu thresholding followed by morphological analysis to obliterate the undesirable artifacts that may be present at the saliency detection stage. Extensive experiments were conducted on the available benchmarking datasets to validate the performance of the segmentation method. Experimental results generally indicate that it is passable to segment skin lesions in dermoscopic images without preprocessing because the applied segmentation method is ferociously competitive with each of the numerous leading supervised and unsupervised segmentation methods investigated in this study.

show abstract

Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

View full text Add to dashboard Cite

show abstract

“…In the literature, a significant amount of traditional [30,40,41] and modern (CNN) [9,11,42,43] medical segmentation techniques have been used. Implementation of the traditional segmentation techniques is time consuming, and most of the existing traditional techniques are semi-automated methods and frequently need operator assistance.…”

Section: Cnn Schemementioning

confidence: 99%

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

View full text Add to dashboard Cite

Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

show abstract

“…Since the RT-PCR test has a sensitivity of 60-70%, symptoms are determined by using lung and chest radiology images in the diagnosis process [8,9]. CT is a frequently used and successful imaging method in diagnosis [10]. The main purpose of computer-aided automatic diagnosis systems is to assist medical professionals in their decisions.…”

Section: Introductionmentioning

confidence: 99%

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Saygılı

2021

Arab J Sci Eng

View full text Add to dashboard Cite

COVID-19 is a virus that has been declared an epidemic by the world health organization and causes more than 2 million deaths in the world. To achieve this, computer-aided automatic diagnosis systems are created on medical images. In this study, an image processing and machine learning-based method is proposed that enables segmenting of CT images taken from COVID-19 patients and automatic detection of the virus through the segmented images. The main purpose of the study is to automatically diagnose the COVID-19 virus. The study consists of three basic steps: preprocessing, segmentation and classification. Image resizing, image sharpening, noise removal, contrast stretching processes are included in the preprocessing phase and segmentation of images with Expectation-Maximization-based Gaussian Mixture Model in the segmentation phase. In the classification stage, COVID-19 is classified as positive and negative by using kNN, decision tree, and two different ensemble methods together with the kernel support vector machines method. In the study, two different CT datasets that are open to the public and a mixed dataset created by combining these datasets were used. The best accuracy values for Dataset-1, Dataset-2 and Mixed Dataset are 98.5%, 86.3%, 94.5%, respectively. The achieved results prove that the proposed approach advances state-of-the-art performance. Within the scope of the study, a GUI that can automatically detect COVID-19 has been created.

show abstract

Fuzzy C-Means Clustering with Histogram based Cluster Selection for Skin Lesion Segmentation using Non-Dermoscopic Images

Cited by 22 publications

References 21 publications

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Computer-Aided Detection of COVID-19 from CT Images Based on Gaussian Mixture Model and Kernel Support Vector Machines Classifier

Contact Info

Product

Resources

About