Advanced stage glioma is the most aggressive form of malignant brain tumors with a short survival time. Real-time pathology assisted, or image guided surgical procedures that eliminate tumors promise to improve the clinical outcome and prolong the lives of patients. Our work is focused on the development of a rapid and sensitive assay for intraoperative diagnostics of glioma and identification of optical markers essential for differentiation between tumors and healthy brain tissues. We utilized fluorescence lifetime imaging (FLIM) of endogenous fluorophores related to metabolism of the glioma from freshly excised brains tissues. Macroscopic time-resolved fluorescence images of three intracranial animal glioma models and surgical samples of patients' glioblastoma together with the white matter have been collected. Several established and new algorithms were applied to identify the imaging markers of the tumors. We found that fluorescence lifetime parameters characteristic of the glioma provided background for differentiation between the tumors and intact brain tissues. All three rat tumor models demonstrated substantial differences between the malignant and normal tissue. Similarly, tumors from patients demonstrated statistically significant differences from the peritumoral white matter without infiltration. While the data and the analysis presented in this paper are preliminary and further investigation with a larger number of samples is required, the proposed approach based on the macroscopic FLIM has a high potential for diagnostics of glioma and evaluation of the surgical margins of gliomas.

/pdf/label-free-macroscopic-fluorescence-lifetime-imaging-of-1wnqgzb4i8.pdf

Label-Free Macroscopic Fluorescence Lifetime Imaging of Brain Tumors.

Fruit quality might suffer from bruises before and after harvest. Global focus is being paid to fruit bruise detection, especially early damage detection. In this study, a method was developed to identify bruised pomegranate fruit (cv. 'Wonderful') immediately after bruising. After splitting 60 pomegranates into bruised and unbruised parts, hyperspectral images were taken immediately, 7 days, and 14 days afterwards. Two 30-fruit groups were sampled. Group A had bruised pomegranates (dropped from a height of 60 cm onto a ceramic surface), whereas Group B had unbruised counterparts. The study analyzed a broad spectrum of wavelengths to collect vital information regarding the effects of injury. This was accomplished with Vis-NIR (400–1000 nm) and SWIR (1000–2500 nm) line scan mode equipment. The line scan mode was selected due to its compatibility with conveyor belt systems typically utilized in fruit packaging lines. The classification prediction model employed the 2-Layer Feedforward artificial neural network due to its advantageous characteristics of simplicity and robustness. This study confirmed early bruise detection for pomegranate fruit with an accuracy of 88.3% and 86.7% based on full and selected wavelengths, respectively. Storage duration improves bruise recognition. Bruises are hard to spot early and become more visible with time. Hence, this technique's capacity to do so is a major benefit in the post-harvest handling. This research reduced the VNIR and SWIR input dimensions from 186 and 288 to five, resulting in a quicker and more compact classification algorithm. This will make it easier to create a cutting-edge sorting and grading system for bruise detection. 

Early bruises detection on pomegranate (Punica granatum L.), using hyperspectral imaging coupled with artificial neutral network algorithm

This study proposes a unique segmentation-based clustering algorithm that utilises k-means for segmentation, further uses a local feature selection technique to obtain the top bands for each cluster and deploys clustering on segmented hyperspectral imagery. The suggested methodology is a framework with several stages. k-means is initially utilized for image segmentation. From the obtained segments, significant segments are identified using Gini impurity. Finally, the cluster map is obtained by merging insignificant clusters with significant clusters. This step also makes use of novel local feature selection strategy. Three sets of hyperspectral images are used in investigations to evaluate the efficiency of the proposed methodology. For assessment, the criteria Normalized Mutual Information and Purity score are utilised. The investigation findings demonstrate that the proposed methodology outperforms the other segmentation methodologies that were compared. According to the results, using band selection and redundancy strategies significantly improves accuracy. 

Hyper Spectral Image Clustering and Local Feature Selection using Gini Impurity

This study proposes a unique segmentation-based clustering algorithm that utilises k-means for segmentation, further uses a local feature selection technique to obtain the top bands for each cluster and deploys clustering on segmented hyperspectral imagery. The suggested methodology is a framework with several stages. k-means is initially utilized for image segmentation. From the obtained segments, significant segments are identified using Gini impurity. Finally, the cluster map is obtained by merging insignificant clusters with significant clusters. This step also makes use of novel local feature selection strategy. Three sets of hyperspectral images are used in investigations to evaluate the efficiency of the proposed methodology. For assessment, the criteria Normalized Mutual Information and Purity score are utilised. The investigation findings demonstrate that the proposed methodology outperforms the other segmentation methodologies that were compared. According to the results, using band selection and redundancy strategies significantly improves accuracy.

Diagnosis is necessary to confirm SARS-CoV-2 infection. Imaging methods are vital in aiding the diagnosis, assessing disease severity, directing treatment, identifying complications, and monitoring the response to treatment. The lungs are the foremost organ impacted, and chest X-rays are the preferred initial imaging exam due to their accessibility and cost-effectiveness, whether obtained at conventional X-ray centers or using portable equipment. This research study has used a combination of virus-infected people as a dataset. The classification of diseases will help doctors to treat the patients and save their life. For classifying Normal, COVID-19 and Viral Pneumonia classes’ the architecture of the pre-trained network XceptionNet is modified. XceptionNet is the method to use for the classification of diseases. The proposed model has resulted in a 98.80% accuracy for classifying the diseases.

Improved Classification for Corona Virus Disease using XceptionNet in X-Ray Images

A new framework for advanced machine learning-based analysis of hyperspectral datasets HSKL was built using the well-known package scikit-learn. In this paper, we describe HSKL’s structure and basic usage. We also showcase the diversity of models supported by the package by applying 17 classification algorithms and measure their baseline performance in segmenting objects with highly similar spectral properties.

HSKL: A Machine Learning Framework for Hyperspectral Image Analysis

Selection of Hyperspectral Endmember Extraction Algorithm for Tumor Delineation in Animal Models

Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entire software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features.

Idcube Lite – A Free Interactive Discovery Cube Software for Multi And Hyperspectral Applications

The shortwave infrared (SWIR) shows a significantly higher level of contrast between different types of tissues compared to the Vis-NIR. This contrast can be further enhanced by using hyperspectral imaging (HSI). Unlike conventional imaging methods, each HSI pixel contains a full high-resolution spectral signal. Complex computational approaches are required to analyze HSI-measured optical alterations. Many of these approaches including machine learning and deep learning algorithms have been developed in the field of geospatial imaging. We adapted and optimized algorithms used in remote sensing to build an image processing platform IDCube that processes biomedical data and identifies tissues abnormalities in SWIR. 

Hyperspectral imaging in SWIR: Applications of geospatial approaches to biological imaging (Conference Presentation)

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HSKL: A Machine Learning Framework for Hyperspectral Image Analysis

Selection of Hyperspectral Endmember Extraction Algorithm for Tumor Delineation in Animal Models

Idcube Lite – A Free Interactive Discovery Cube Software for Multi And Hyperspectral Applications

Hyperspectral imaging in SWIR: Applications of geospatial approaches to biological imaging (Conference Presentation)

Idcube Lite – A Free Interactive Discovery Cube Software for Multi And Hyperspectral Applications