Medical image enhancement based on histogram algorithms

Salem, Nema; Malik, Hebatullah M.; Shams, Asmaa

doi:10.1016/j.procs.2019.12.112

Cited by 81 publications

(36 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In an X-ray image, the anisotropic diffusion is used for smoothing fine details and remove the undesirable low contrast and brightness. In addition, the histogram equalization technique can be used to overcome the low contrast problem ( Salem et al, 2019 ). It will work as an efficient contrast enhancement technique in medical imaging operations.…”

Section: Methodsmentioning

confidence: 99%

Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features

Mostafiz

Uddin

Alam

et al. 2022

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

Section: Methodsmentioning

confidence: 99%

Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features

Mostafiz

Uddin

Alam

et al. 2022

Journal of King Saud University - Computer and Information Scie

View full text Add to dashboard Cite

“…Histogram is representing a graph of the probability distribution of gray values in a digital image. Visualization of the histogram image can help to analyze the frequency of gray levels contained in the image [25]. The parameters to be calculated are mean, standard deviation, skewness, and entropy [26], [27], whereas the GLCM parameters calculated are contrast parameters, energy parameters, and homogeneity parameters.…”

Section: Methodsmentioning

confidence: 99%

Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

Djunaidi¹,

Agtriadi²,

Kuswardani³

et al. 2021

IJEECS

View full text Add to dashboard Cite

One way to detect breast cancer is using the Ultrasonography (USG) procedure, but the ultrasound image is susceptible to the noise speckles so that the interpretation and diagnosis results are different. This paper discusses the classification of breast cancer ultrasound images that aims to improve the accuracy of the identification of the type and level of cancer malignancies based on the features of its texture. The feature extraction process uses a <em>histogram</em> which then the results are calculated using the Gray Level Co-Occurrence Matrix (GLCM). The results of the two extraction features are then classified using K-Nearest Neighbors (KNN) to obtain accurate figures from those images. The results of this study is that the accuracy in detecting cancer types is 80%.

show abstract

“…Thus it is of paramount importance to select appropriate and application adaptive approaches in order to increase contrast, decrease information loss and artifacts from captured image data. Histogram-based techniques constitute a common yet quite effective tool for medical image enhancement, finding numerous applications and algorithmic modifications in international literature for many years [33,34]. The latest advances in magnetic resonance (MR) image enhancement include metaheuristics and particle swarm optimization (PSO).…”

Section: Literature Reviewmentioning

confidence: 99%

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Deshpande

Cambria

Barnes

et al. 2021

Signals

View full text Add to dashboard Cite

This study investigates the potential of fluorescence imaging in conjunction with an original, fused segmentation framework for enhanced detection and delineation of brain tumor margins. By means of a test bed optical microscopy system, autofluorescence is utilized to capture gray level images of brain tumor specimens through slices, obtained at various depths from the surface, each of 10 µm thickness. The samples used in this study originate from tumor cell lines characterized as Gli36ϑEGRF cells expressing a green fluorescent protein. An innovative three-step biomedical image analysis framework is presented aimed at enhancing the contrast and dissimilarity between the malignant and the remaining tissue regions to allow for enhanced visualization and accurate extraction of tumor boundaries. The fluorescence image acquisition system implemented with an appropriate unsupervised pipeline of image processing and fusion algorithms indicates clear differentiation of tumor margins and increased image contrast. Establishing protocols for the safe administration of fluorescent protein molecules, these would be introduced into glioma tissues or cells either at a pre-surgery stage or applied to the malignant tissue intraoperatively; typical applications encompass areas of fluorescence-guided surgery (FGS) and confocal laser endomicroscopy (CLE). As a result, this image acquisition scheme could significantly improve decision-making during brain tumor resection procedures and significantly facilitate brain surgery neuropathology during operation.

show abstract

Medical image enhancement based on histogram algorithms

Cited by 81 publications

References 5 publications

Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features

Covid-19 detection in chest X-ray through random forest classifier using a hybridization of deep CNN and DWT optimized features

Gray level co-occurrence matrix feature extraction and histogram in breast cancer classification with ultrasonographic imagery

Fluorescent Imaging and Multifusion Segmentation for Enhanced Visualization and Delineation of Glioblastomas Margins

Contact Info

Product

Resources

About