Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph, Seena; Olugbara, Oludayo O.

doi:10.3390/jimaging7090187

Cited by 3 publications

(6 citation statements)

References 142 publications

(348 reference statements)

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“…The results of this study have demonstrated the capability of the CHC-Otsu algorithm to accurately segment skin lesions in dermoscopic images of varying undesirable properties. The robustness of the results obtained is a consequence of the unique integration of saliency features of color contrast, contrast ratio, spatial feature, and center prior in the CHC algorithm [ 42 ]. It is worth noting that the CHC-Otsu algorithm successfully addresses the inherent complexities of dermoscopy images without the inclusion of preprocessing.…”

Section: Discussionmentioning

confidence: 99%

“…The CHC-Otsu algorithm is a saliency segmentation method used to investigate the effect of preprocessing on its performance for the analysis of skin lesions. The algorithm is an integration of the CHC algorithm [ 42 ] with the Otsu thresholding algorithm [ 43 ] for saliency segmentation of skin lesions. Saliency segmentation methods were inspired by their ability to retrieve the most conspicuous objects from the background information in a manner reminiscent of the human visual system by observing the local or global visual rarities such as color, intensity, contrast, and brightness [ 73 , 74 , 75 , 76 ].…”

Section: Methodsmentioning

confidence: 99%

“…A cluster shows high saliency when it has a high contrast to the nearby clusters than the distant clusters [ 82 ]. The contrast ratio is used to emphasize the high color contrast difference between the maximum and minimum brightness of clusters [ 42 ]. The spatial feature is the spatial correlation of a cluster to all other image clusters.…”

Section: Methodsmentioning

confidence: 99%

“…The MATLAB (2019a, The MathWorks, Inc., Natick, MA, USA) norm function used in steps 30 and 44 implements the Euclidean distance. This algorithmic description of the CHC-Otsu Algorithm 1 is based on the mathematical formulation of the CHC Algorithm [ 42 ]. Algorithm 1.

…”

Section: Methodsmentioning

confidence: 99%

“…The principal objective of this study was to investigate the effect of image preprocessing on the performance of a saliency segmentation method for skin lesions. The objective was accomplished by leveraging the collaboration of color histogram clustering [ 42 ] with Otsu thresholding [ 43 ] to realize the CHC-Otsu algorithm. The DullRazor artifact removal algorithm [ 44 ] and contrast limited adaptive histogram equalization (CLAHE) [ 45 , 46 ] are the two famous preprocessing methods utilized for experimentation in this study.…”

Section: Introductionmentioning

confidence: 99%

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Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

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Experimental comparison of ten state-of-the-art saliency detection algorithms for segmenting leukocyte nucleus

Joseph

Olugbara

2022

2022 Conference on Information Communications Technology and Society (ICTAS)

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Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint

Ndayikengurukiye

Mignotte

2022

J. Imaging

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The effortless detection of salient objects by humans has been the subject of research in several fields, including computer vision, as it has many applications. However, salient object detection remains a challenge for many computer models dealing with color and textured images. Most of them process color and texture separately and therefore implicitly consider them as independent features which is not the case in reality. Herein, we propose a novel and efficient strategy, through a simple model, almost without internal parameters, which generates a robust saliency map for a natural image. This strategy consists of integrating color information into local textural patterns to characterize a color micro-texture. It is the simple, yet powerful LTP (Local Ternary Patterns) texture descriptor applied to opposing color pairs of a color space that allows us to achieve this end. Each color micro-texture is represented by a vector whose components are from a superpixel obtained by the SLICO (Simple Linear Iterative Clustering with zero parameter) algorithm, which is simple, fast and exhibits state-of-the-art boundary adherence. The degree of dissimilarity between each pair of color micro-textures is computed by the FastMap method, a fast version of MDS (Multi-dimensional Scaling) that considers the color micro-textures’ non-linearity while preserving their distances. These degrees of dissimilarity give us an intermediate saliency map for each RGB (Red–Green–Blue), HSL (Hue–Saturation–Luminance), LUV (L for luminance, U and V represent chromaticity values) and CMY (Cyan–Magenta–Yellow) color space. The final saliency map is their combination to take advantage of the strength of each of them. The MAE (Mean Absolute Error), MSE (Mean Squared Error) and Fβ measures of our saliency maps, on the five most used datasets show that our model outperformed several state-of-the-art models. Being simple and efficient, our model could be combined with classic models using color contrast for a better performance.

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Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Cited by 3 publications

References 142 publications

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Experimental comparison of ten state-of-the-art saliency detection algorithms for segmenting leukocyte nucleus

Salient Object Detection by LTP Texture Characterization on Opposing Color Pairs under SLICO Superpixel Constraint

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