Foreground and Background Feature Fusion Using a Convex Hull Based Center Prior for Salient Object Detection

Afzali, Shima; Al-Sahaf, Harith; Xue, Bing; Hollitt, Christopher; Zhang, MengJie

doi:10.1109/ivcnz.2018.8634726

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The spatial feature is the spatial correlation of a cluster to all other image clusters. The center prior is the distance of each cluster to the image center to emphasize a low weight for the cluster framed near the image boundary [ 80 , 83 , 84 , 85 , 86 ].…”

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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“…Normal RBCs and ovalocytes have a round smooth shape, whereas echinocytes have a round bumpy protruding shape. By analyzing these characteristics using the convex hull algorithm, the number of convex hulls required to create a convex polygon including all points on a two‐dimensional plane was used as a feature (Afzali et al, 2018). Moreover, in the case of normal RBCs and ovalocytes with a round smooth shape, the difference between the area of the convex polygon to which the convex hull algorithm is applied and the area of the original RBC is not considerable.…”

Section: Methodsmentioning

confidence: 99%

Automatic analysis system for abnormal red blood cells in peripheral blood smears

Gil

Moon

Lee

et al. 2022

Microscopy Res & Technique

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The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time-consuming because the RBCs are manually classified. In addition, because the classification depends on the subjective criteria of pathologists, objective classification is difficult to achieve. In this paper, an automatic classification method that is solely based on images of RBCs captured under a microscope and processed using machine learning (ML) is proposed. The size and hemoglobin abnormalities of RBCs were classified by optimizing the criteria used in clinical practice. For morphologically abnormal RBCs classification, used seven geometric features information (major axis, minor axis, ratio of major and minor axis, perimeter, circularity, number of convex hulls, difference between area and convex area) and five types of multiple classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Adaboost models). Among was categorized using SVM, highly accurate results (99.9%) were obtained. The classification is performed simultaneously, and results are provided to the user through a graphical user interface (GUI).

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“…Suitable prior knowledge can enhance the quality of saliency detection, but the ultimate results are not absolute on images with complex background and foreground objects that possess variable shapes, sizes, locations, and appearances. The center prior methods are not sufficient to trace salient objects when the image background is framed near the image center or salient objects are close to the image boundary [36,47,59,79]. The methods of exploiting background and connectivity priors have suffered from incorrect suppression of salient objects that touch image boundary [6,32,79,80].…”

Section: Global Contrast-based Saliency Detectionmentioning

confidence: 99%

“…In the context of this study, it measures the difference between the maximum and minimum brightness of regions in an image. φ(r i , r j ) = CC(r i ) + 0.05 CC(r j ) + 0.05 (5) The significance of center prior in saliency detection as given by Equation ( 6) has been highlighted in literature following the fundamental assumption that salient objects are framed near the image center while background pixels are distributed at the image borders [36,59,66,68,102]. It is usually formulated and extensively used in literature as a Gaussian distribution [3,36,66,106,113].…”

Section: Calculation Of Region Saliencymentioning

confidence: 99%

“…Examples of image categories are salient objects with erratic sizes, positions, and counts, cluttered backgrounds, and low dissimilarity among regions of heterogeneous foreground or heterogeneous background. The task of completely highlighting salient objects in different image categories is still not adequately resolved in most of the existing saliency methods [58][59][60]. The other major challenge is the mitigation of computational complexity because salient object detection is an essential preprocessing stage in computer vision.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

Joseph

Olugbara

2021

J. Imaging

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Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.

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Foreground and Background Feature Fusion Using a Convex Hull Based Center Prior for Salient Object Detection

Cited by 7 publications

References 15 publications

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Automatic analysis system for abnormal red blood cells in peripheral blood smears

Detecting Salient Image Objects Using Color Histogram Clustering for Region Granularity

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