Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

Review of Artificial Intelligence-based COVID-19 Detection and A CNN-based Model to Detect Covid-19 from X-Rays and CT images

Various diseases are rising in the world in different regions. Each disease is diagnosed through its signs, & symptoms, and is cured accordingly. Some persons have immunity to fight against such diseases, but most of the persons become the victim of these diseases. The epidemic in China triggered by a novel coronavirus (Covid-19) presents an unprecedented danger to general safety, worldwide. Covid-19 has a more rapid transmission rate. A speedy symptomatic standard check to identify the infectious disease is required to prevent its spread. In an existing situation, testing kits of Covid-19 are available in less quantity and they require significant time to produce outcomes. The purpose of this research is to explore recently reported techniques for automated identification of Covid-19 from medical images and to report an efficient method for the detection of Covid-19 from digital X-Ray and computed tomography images. The proposed model can assist in the identification of Covid-19 at its initial level in lesser time. Publically available and locally developed datasets have been used for research and experiments. The highest classification accuracy achieved through the reported model is 99.40%

Water Classification Using Convolutional Neural Network

The classification of water sources is a challenging task due to the low contrast texture features, the visual similarities between them, and the causes posed by image acquisition with different camera angles and placements. The various image enhancement techniques, i.e., Unsharp Masking (UM), Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Contrast Stretching, were used to highlight the contrast and texture features of water images. The enhanced image samples were then fed to the proposed Convolutional Neural Network (CNN)-based model named WaterNet (WNet) for classification. From all employed image enhancement techniques, Contrast Limited Adaptive Histogram Equalization (CLAHE) provides better results in terms of contrast and texture features of water. CLAHE also improved the classification performance of the proposed model, with an accuracy of 97%. For comparison, experiments have also been performed on state-of-the-art pre-trained models, which are DenseNet-201, Inception_ResNet_v2, Inception_v3, and Mobile-Net. Comparison shows that the proposed technique achieves better accuracy in comparison with the state-of-the-art methods

Showing results of 3D processing of proposed segmentation method.

<p>(A)–(E) Selected transaxial slices of T2 brain MR data set. (F)–(J) Colorized soft segmentation of slices (A)–(E).</p

Block diagram of proposed methods.

<p>(A) Block diagram of proposed colorization method. (B) Block diagram of proposed segmentation method.</p

Probabilistic histogram and detailed description of Figure 3.

<p>(A) Histogram of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033616#pone-0033616-g003" target="_blank">Figure 3(A)</a>. (B) Lines drawn around the peaks for probabilistic histogram. (C) Probabilistic histogram. (D) Splitting region for ROI at intersection points.</p

Results of our proposed colorization method with other medical imaging modalities.

<p>(A) OCT image of nail under skin. (B) Digital X-Ray of knee. (C) CT image of thorax. (D) Mammographic image. (E)–(H) Colorized images of (A)–(D) with our proposed method respectively.</p

Color representation of T2 brain MR image with our proposed colorization method.

<p>(A) Abnormal T2 brain MR image of patient aged 32. (B) Color transformed image. (C) Selected centroids with proposed method.</p

Comparison and Jaccard Coefficient of GM, WM and CSF in pixels (Avg).

<p>Comparison and Jaccard Coefficient of GM, WM and CSF in pixels (Avg).</p