An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification

Nazir

Microscopy Res & Technique

et al. 2019

Self Cite

The emergence of cloud infrastructure has the potential to provide significant benefits in a variety of areas in the medical imaging field. The driving force behind the extensive use of cloud infrastructure for medical image processing is the exponential increase in the size of computed tomography (CT) and magnetic resonance imaging (MRI) data. The size of a single CT/MRI image has increased manifold since the inception of these imagery techniques. This demand for the introduction of effective and efficient frameworks for extracting relevant and most suitable information (features) from these sizeable images. As early detection of lungs cancer can significantly increase the chances of survival of a lung scanner patient, an effective and efficient nodule detection system can play a vital role. In this article, we have proposed a novel classification framework for lungs nodule classification with less false positive rates (FPRs), high accuracy, sensitivity rate, less computationally expensive and uses a small set of features while preserving edge and texture information. The proposed framework comprises multiple phases that include image contrast enhancement, segmentation, feature extraction, followed by an employment of these features for training and testing of a selected classifier. Image preprocessing and feature selection being the primary steps—playing their vital role in achieving improved classification accuracy. We have empirically tested the efficacy of our technique by utilizing the well‐known Lungs Image Consortium Database dataset. The results prove that the technique is highly effective for reducing FPRs with an impressive sensitivity rate of 97.45%.

Section: Introductionmentioning

confidence: 99%

Lungs nodule detection framework from computed tomography images using support vector machine

Nazir

Microscopy Res & Technique

et al. 2019

Self Cite

“…Figure 10 shows a comparison examples between the proposed method segmentation results and other representative methods that used the four datasets. We compare a sample image from PH2, ISBI 2016, ISBI 2017, and DermIS datasets with the work of Khan et al [40] (row 1), Yu et al [34] (row 2), Guo et al [22] (row 3), and Dey et al [6] (row 4). It can be observed that the proposed method surpassed these methods and the difference clear in the skin lesion segmentation border.…”

Section: Resultsmentioning

confidence: 99%

Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm

et al. 2018

Abstract:The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95. 24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.

“…A hierarchical inference technique is used to deal with scale variation of lesions. Khan et al () introduced a novel lesion detection and classification technique based on probabilistic distribution segmentation and controlled entropy‐based feature selection. The results are using two segmentation techniques: uniform distribution and normal distribution, which are fused and get the final segmented region.…”

Section: Related Workmentioning

confidence: 99%

“…The number of diagnosed patients with skin cancer is increased drastically from the last few years (Afza, Khan, Sharif, & Rehman, ; Pathan, Prabhu, & Siddalingaswamy, ). Two major types of skin cancer exist: melanoma and benign (Khan et al, ; Saba, Khan, Rehman, & Marie‐Sainte, ). Among all skin cancer types, melanoma is one of the deadliest that contributes more deaths annually among young peoples.…”

Section: Introductionmentioning

confidence: 99%

Microscopic melanoma detection and classification: A framework of pixel‐based fusion and multilevel features reduction

Rehman

Microscopy Res & Technique

Mehmood

et al. 2020

The numbers of diagnosed patients by melanoma are drastic and contribute more deaths annually among young peoples. An approximately 192,310 new cases of skin cancer are diagnosed in 2019, which shows the importance of automated systems for the diagnosis process. Accordingly, this article presents an automated method for skin lesions detection and recognition using pixel‐based seed segmented images fusion and multilevel features reduction. The proposed method involves four key steps: (a) mean‐based function is implemented and fed input to top‐hat and bottom‐hat filters which later fused for contrast stretching, (b) seed region growing and graph‐cut method‐based lesion segmentation and fused both segmented lesions through pixel‐based fusion, (c) multilevel features such as histogram oriented gradient (HOG), speeded up robust features (SURF), and color are extracted and simple concatenation is performed, and (d) finally variance precise entropy‐based features reduction and classification through SVM via cubic kernel function. Two different experiments are performed for the evaluation of this method. The segmentation performance is evaluated on PH2, ISBI2016, and ISIC2017 with an accuracy of 95.86, 94.79, and 94.92%, respectively. The classification performance is evaluated on PH2 and ISBI2016 dataset with an accuracy of 98.20 and 95.42%, respectively. The results of the proposed automated systems are outstanding as compared to the current techniques reported in state of art, which demonstrate the validity of the proposed method.