Image pre-processing in computer vision systems for melanoma detection

Vocaturo, Eugenio; Zumpano, Ester; Veltri, Pierangelo

doi:10.1109/bibm.2018.8621507

Cited by 36 publications

(18 citation statements)

References 23 publications

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“…The dataset of non-dermoscopic images used in this study, taken from The International Skin Imaging Collaboration (ISIC), was created for ISIC 2019 challenge [15][16][17][18]. As it shown in Figure 2, according to the type of disease the images can be divided into 8 categories: Basal cell carcinoma (BCC), Melanoma (MEL), Squamous cell carcinoma (SCC), Vascular lesion (VASC), Dermatofibroma (DF), Actinic keratosis (AK), Benign keratosis (solar lentigo / seborrheic keratosis/lichen planus-like keratosis) (BKL), and Melanocytic nevus (NV) [18][19]. It is to be noted that, BCC, MEL, and SCC are Cancerous tumours, while the others are Benign (Non-Cancerous) Tumours.…”

Section: Methodsmentioning

confidence: 99%

Cilt Kanseri Teşhisi için Konvolüsyonel Sinir Ağları Tabanlı Bilgisayar Destekli Tanıda (CNN-CAD) Dijital Görüntü Kalitesinin İyileştirilmesi

YALÇIN

Gursel

2021

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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The practice of detecting skin cancer is based primarily on a visual examination by a dermatologist, followed by a series of tests for a more accurate diagnosis. The concept "the earlier cancer is detected in its natural history, the more effective the treatment is likely to be" is also valid for skin cancer. Hence, any delayed or missed diagnosis can lead to a more severe clinical stage or, what's worse, death. On the other hand, the lack of biomarkers in clinical use brings about overdiagnosis and unnecessary biopsies. DL-CAD system seems to be an excellent candidate for improving diagnostic accuracy and reducing unnecessary treatments. However, the vast majority of conventional CADs manipulate dermoscopic images, which require not only costly equipment but also time-consuming processing. Despite the difficulties with precision, state-of-the-art DL-CAD systems provide an interpretation using digital images, requiring no expertise in cost-effective dermoscopic image capture and interpretation. Preprocessing methods play a crucial role in solving this problem. This study presents results with regard to pre-processing steps to improve the images to be used in the diagnosis of the 5 most common skin cancer types for the proposed CNN based ResNet50 deep learning model. To the best of our knowledge it is the first time that ResNet50 deep-learning model has been utilized in diagnosis of skin cancer.

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Section: Methodsmentioning

confidence: 99%

Cilt Kanseri Teşhisi için Konvolüsyonel Sinir Ağları Tabanlı Bilgisayar Destekli Tanıda (CNN-CAD) Dijital Görüntü Kalitesinin İyileştirilmesi

YALÇIN

Gursel

2021

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

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“…[41] Artifact removal using Frangi Vesselness filter and image enhancement with contrast-limited adaptive histogram equalization. [47] Artifact removal using DullRazor and image enhancement with adaptive histogram equalization.…”

Section: Studymentioning

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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“…The strategies referred to, as well as the other solutions presented in the literature, have as their final objective that of being able to favor the segmentation of the image and the extraction of functional features to the classification process. The strategies to be adopted depend on the nature of the considered images and of the impurities that must be managed [23].…”

Section: Image Pre-processingmentioning

confidence: 99%

“…On the contrary, the objective of our experimentation consists in simply evaluating the classification results, obtainable on 200 images (100 melanomas and 100 common nevi) drawn from the same database used in [34], but without performing any pre-processing step aimed at cleaning up the images from possible noises [23,35]. This choice is motivated by the necessity to investigate on the possibility to create fast self-diagnosis systems for accessible skin lesions.…”

Section: Numerical Experimentationsmentioning

confidence: 99%

Melanoma detection using color and texture features in computer vision systems

Fuduli¹,

Veltri²,

Vocaturo³

et al. 2019

Adv. sci. technol. eng. syst. j.

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All forms of skin cancer are becoming widespread. These forms of cancer, and melanoma in particular, are insidious and aggressive and if not treated promptly can be lethal to humans. Effective treatment of skin lesions depends strongly on the timeliness of the diagnosis: for this reason, artificial vision systems are required to play a crucial role in supporting the diagnosis of skin lesions. This work offers insights into the state of the art in the field of melanoma image classification. We include a numerical section where a preliminary analysis of some classification techniques is performed, using color and texture features on a data set constituted by plain photographies, to which no pre-processing technique has been applied. This is motivated by the necessity to open new horizons in creating self-diagnosis systems for accessible skin lesions, due also to a huge innovation of cameras, smartphones technology and wearable devices.

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Image pre-processing in computer vision systems for melanoma detection

Cited by 36 publications

References 23 publications

Cilt Kanseri Teşhisi için Konvolüsyonel Sinir Ağları Tabanlı Bilgisayar Destekli Tanıda (CNN-CAD) Dijital Görüntü Kalitesinin İyileştirilmesi

Cilt Kanseri Teşhisi için Konvolüsyonel Sinir Ağları Tabanlı Bilgisayar Destekli Tanıda (CNN-CAD) Dijital Görüntü Kalitesinin İyileştirilmesi

Preprocessing Effects on Performance of Skin Lesion Saliency Segmentation

Melanoma detection using color and texture features in computer vision systems

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