A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information

Nikbakhsh, Navid; Baleghi, Yasser; Agahi, Hamzeh

doi:10.1007/s00138-020-01130-0

Cited by 23 publications

(10 citation statements)

References 35 publications

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“…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 ]. It ideally induces a multidimensional color image into a grayscale image that is naturally amenable to Otsu thresholding.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Joseph

Olugbara

2022

Diagnostics

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“…These included stems, cereals, roots, soil, petals and water pipes as part of the environment in which the images were captured. These non-leaf regions [2] were removed using an RGB color space model [20]. The green channel level in the model was used to effectively remove pixels that were not regarded as part of a leaf region.…”

Section: Image Preprocessingmentioning

confidence: 99%

“…They are often characterized by having leaves, stems, roots, and seed-producing capabilities [1]. In particular, the outward appearance of plant leaves is a source of vital information towards ascertaining the wellbeing and productivity of a plant [2]. Thus, developing methods to effectively segment a single plant leaf from a complex background of other leaves is an important step toward the realization of improved precision agricultural systems.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, developing methods to effectively segment a single plant leaf from a complex background of other leaves is an important step toward the realization of improved precision agricultural systems. For example, the segmentation of a single leaf is important for conducting detailed plant analysis, recognition of crop species, detection and identification of crop diseases, weed detection, plant growth assessment and monitoring diverse plant conditions to improve agricultural productivity and yield [2,3]. The segmentation process involves separating the leaf region of a plant or an entire individual leaf from an image with a complex background with similar textures, colors and shapes.…”

Section: Introductionmentioning

confidence: 99%

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Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

et al. 2021

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The segmentation of a single leaf from an image with overlapping leaves is an important step towards the realization of effective precision agricultural systems. A popular approach used for this segmentation task is the hybridization of the Chan-Vese model and the Sobel operator CV-SO. This hybridized approach is popular because of its simplicity and effectiveness in segmenting a single leaf of interest from a complex background of overlapping leaves. However, the manual threshold and parameter tuning procedure of the CV-SO algorithm often degrades its detection performance. In this paper, we address this problem by introducing a dynamic iterative model to determine the optimal parameters for the CV-SO algorithm, which we dubbed the Dynamic CV-SO (DCV-SO) algorithm. This is a new hybrid automatic segmentation technique that attempts to improve the detection performance of the original hybrid CV-SO algorithm by reducing its mean error rate. The results obtained via simulation indicate that the proposed method yielded a 1.23% reduction in the mean error rate against the original CV-SO method.

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“…Segmentation is one of the prime parts of image processing. Boundary detection plays an important role in machine vision applications, such as control of urban transportation systems [1], video surveillance [2], medical diagnosis [3][4][5][6], identifying military targets [7], plants monitoring [8][9][10] and object tracking [11][12][13][14][15]. One of the important areas in image segmentation is the division of an image into areas with different textural features [16].…”

Section: Introductionmentioning

confidence: 99%

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

2022

IJE

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Texture image segmentation plays an important role in various computer vision tasks. Active contour models are one of the most efficient and popular methods for identifying the purpose and segmentation of objects in the image. This paper presents a parametric active contour model (PACM) with a robust minimization framework based on image texture energy. First, the texture features of the original image are extracted using gray level co-occurrence matrix (GLCM). Subsequently, based on the GLCM texture features inside and outside the active contour, Jensen-Tsallis divergence of energies is calculated. The Jensen-Tsallis divergence is added to the parametric active contour using the balloon equation. The divergence is maximum at the boundary between the foreground and background of the image, which results in minimizing the active contour equation at the boundary of the target object. This global minimization energy function with texture feature can avoid the existence of local minima in the PACM models. Also, as opposed to previous models, the proposed model only requires the initial contour and is not dependent on the distance of the initial contour from the target object. In terms of segmentation accuracy and efficiency, experiments with synthetic and natural images demonstrate that the proposed approach obtains more satisfactory results than the previous state-of-the-art methods.

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A novel approach for unsupervised image segmentation fusion of plant leaves based on G-mutual information

Cited by 23 publications

References 35 publications

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Development of Hybrid Automatic Segmentation Technique of a Single Leaf from Overlapping Leaves Image

A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

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