Image Enhancement and Segmentation Techniques for Detection of Knee Joint Diseases: A Survey

Nazir

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: Methodsmentioning

confidence: 99%

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

Nazir

et al. 2019

Self Cite

“…The aforementioned studies discuss the importance of lesion enhancement, which is the one, the primary steps for an accurate classification. For image segmentation, they mostly utilize well‐known techniques such as K‐means, watershed, Fuzzy C‐means, Otsu thresholding and to name a few (Abbas et al, , ; Husham, Alkawaz, Saba, Rehman, & Alghamdi, ; Jamal, Hazim Alkawaz, Rehman, & Saba, ; Norouzi et al, ; Saba et al, ,b; Waheed, Alkawaz, Rehman, Almazyad, & Saba, ). The saliency methods are also employed for lesion segmentation with improved performance.…”

Section: Related Workmentioning

confidence: 99%

Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion

Akram

Sharif

et al. 2019

Self Cite

Skin cancer is being a most deadly type of cancers which have grown extensively worldwide from the last decade. For an accurate detection and classification of melanoma, several measures should be considered which include, contrast stretching, irregularity measurement, selection of most optimal features, and so forth. A poor contrast of lesion affects the segmentation accuracy and also increases classification error. To overcome this problem, an efficient model for accurate border detection and classification is presented. The proposed model improves the segmentation accuracy in its preprocessing phase, utilizing contrast enhancement of lesion area compared to the background. The enhanced 2D blue channel is selected for the construction of saliency map, at the end of which threshold function produces the binary image. In addition, particle swarm optimization (PSO) based segmentation is also utilized for accurate border detection and refinement. Few selected features including shape, texture, local, and global are also extracted which are later selected based on genetic algorithm with an advantage of identifying the fittest chromosome. Finally, optimized features are later fed into the support vector machine (SVM) for classification. Comprehensive experiments have been carried out on three datasets named as PH2, ISBI2016, and ISIC (i.e., ISIC MSK‐1, ISIC MSK‐2, and ISIC UDA). The improved accuracy of 97.9, 99.1, 98.4, and 93.8%, respectively obtained for each dataset. The SVM outperforms on the selected dataset in terms of sensitivity, precision rate, accuracy, and FNR. Furthermore, the selection method outperforms and successfully removed the redundant features.

“…On the same grounds, Somasekar () and Makkapati and Rao () addressed the segmentation of the parasites. However, single RBCs can also have noisy chromatin dots, single dots are not taken into consideration with the aid of experts as parasites, fake consequences might be pronounced and accuracy might be under threat (Saba, ; Saba et al, ,b; Sadad, Munir, Saba, & Hussain, ).…”

Section: Related Workmentioning

confidence: 99%

Plasmodium life cycle stage classification based quantification of malaria parasitaemia in thin blood smears

Abbas

Saba

Rehman

et al. 2018

Self Cite

Visual inspection for the quantification of malaria parasitaemiain (MP) and classification of life cycle stage are hard and time taking. Even though, automated techniques for the quantification of MP and their classification are reported in the literature. However, either reported techniques are imperfect or cannot deal with special issues such as anemia and hemoglobinopathies due to clumps of red blood cells (RBCs). The focus of the current work is to examine the thin blood smear microscopic images stained with Giemsa by digital image processing techniques, grading MP on independent factors (RBCs morphology) and classification of its life cycle stage. For the classification of the life cycle of malaria parasite the k‐nearest neighbor, Naïve Bayes and multi‐class support vector machine are employed for classification based on histograms of oriented gradients and local binary pattern features. The proposed methodology is based on inductive technique, segment malaria parasites through the adaptive machine learning techniques. The quantification accuracy of RBCs is enhanced; RBCs clumps are split by analysis of concavity regions for focal points. Further, classification of infected and non‐infected RBCs has been made to grade MP precisely. The training and testing of the proposed approach on benchmark dataset with respect to ground truth data, yield 96.75% MP sensitivity and 94.59% specificity. Additionally, the proposed approach addresses the process with independent factors (RBCs morphology). Finally, it is an economical solution for MP grading in immense testing.