Screening and diagnosing of the melanoma are crucial for the early diagnosis. As the deterioration of melanoma, it can be easily separated from the other materials based on the spectral features and spatial features. With the image of microscopic hyperspectral, this paper applies spectral math to preprocess the image firstly and the utilizes three traditional supervised classifications-maximum likelihood classification (MLC), convolution neural networks (CNN) and support vector machine (SVM) to make the segmentation after preprocess. Finally, we evaluate the accuracy of results generated by three to get the best segmentation method among them. This experiment shows practical value in pathological diagnosis.
Liver cancer has one of the highest rates of human morbidity and mortality. However, in terms of pathology, liver cancer is traditionally clinically diagnosed based on observation of microscopic images of pathological liver sections. This paper investigates in vitro samples of rat models of bile duct carcinoma and presents a quantitative analysis method based on microscopic hyperspectral imaging technology to evaluate liver cancers at different stages. The example-based feature extraction method used in this paper mainly includes two algorithms: a morphological watershed algorithm is applied to find object and segment pathological components of pathological liver sections at different stages, and a support vector machine algorithm is implemented for liver tumor classification. Majority/minority analysis is utilized as the postclassification tool to eliminate small plaques from the preliminary classification results. Then, pseudocolor synthesis in RGB color space is used to produce the final results. The experimental results show that this method can effectively calculate the percent tumor areas in liver biopsies at different time points, that is, 3.338%, 11.952%, 15.125%, and 23.375% at 8, 12, 16, and 20 weeks, respectively. Notably, through tracking analysis, the processed results of 8-week images showed the possibility for early diagnosis of the liver tumor.
Serious liver fibrosis will develop into liver tumor. Therefore, prevention and early treatment of hepatocellular carcinoma are the focuses of the medical community. To automatically identify and analyze the degree of liver fibrosis, a more intuitive and convenient approach is proposed to segmentation of liver pathological slice images. This paper aims to use hyperspectral image processing technology to analyze the pathological sections of liver tissue cells. The method uses the spectral math for image preprocessing, and utilizes the superior classification ability of neural net (NN) and support vector machines (SVM) to identify the pathological images of liver tissue. On this basis, Majority/Minority Analysis (MMA) is as the post classified tool to weaken small plaques interference. At last the original image and the classification results are synthesized by RGB bands, and good analysis results can be obtained. The experimental results show that the presented method has great practical value in clinical diagnosis.
As an imaging technology used in remote sensing, hyperspectral imaging can provide more information than traditional optical imaging of blood cells. In this paper, an AOTF based microscopic hyperspectral imaging system is used to capture hyperspectral images of blood cells. In order to achieve the segmentation of red blood cells, Gaussian process using squared exponential kernel function is applied first after the data preprocessing to make the preliminary segmentation. The derivative spectrum with spectral angle mapping algorithm is then applied to the original image to segment the boundary of cells, and using the boundary to cut out cells obtained from the Gaussian process to separated adjacent cells. Then the morphological processing method including closing, erosion and dilation is applied so as to keep adjacent cells apart, and by applying median filtering to remove noise points and filling holes inside the cell, the final segmentation result can be obtained. The experimental results show that this method appears better segmentation effect on human red blood cells.
The identification of white blood cells was important as it provided diagnosis information of different kinds of diseases. However, traditional light microscopy based leukocyte cells recognition and segmentation methods usually inaccurate. This paper proposed a hybrid algorithm applied mathematical support vector machine cells screening algorithm combined with BandMax and spectral angle mapping for white blood cell segmentation, that was, it treated BandMax and spectral angle mapping as a new preprocessing method to divide the boundaries between cells, and then used support vector machine cells screening algorithm to segment the hyperspectral cell images more efficiently and precisely than traditional segmentation algorithms. Experimental results shown that the hybrid algorithm provided higher classification accuracy than traditional methods on improving the classification accuracy and effective extraction of white blood cells. By combing both spatial and spectral features, this strategy had been successfully tested for classifying objects among leukocytes, erythrocytes and serums in raw samples, including spectral features reached higher accuracy than any single algorithm cases, with a maximum improvement of nearly 26.82%.
Hyperspectral imaging is an emerging imaging modality for medical applications. It provides more information than traditional optical image for owning two spatial dimensions and one spectral dimension. Multi dimension information of hyperspectral images can be used to classify different tissues and cells, while it’s difficult to distinguish them by traditional methods. The processing method presented in this paper is composed of two main blocks: Support Vector Machine (SVM) algorithm is adopted to identify different components of blood cells through the spectral dimension. In order to make it easy for blood cell counting, some morphological processing methods are used to process images through the spatial dimensions. This strategy, applying SVM and morphological processing methods, has been successfully tested for classifying objects among erythrocytes, leukocytes and serums in raw samples. Experimental results show that the proposed method is effective for red blood cells identification.
A direct spatial and spectral observation of CdSe and CdSe/CdS quantum dots (QDs) as probes in live cells is performed by using a custom molecular hyperspectral imaging (MHI) system. Water-soluble CdSe and CdSe/CdS QDs are synthesized in aqueous solution under the assistance of high-intensity ultrasonic irradiation and incubated with colon cancer cells for bioimaging. Unlike the traditional fluorescence microscopy methods, MHI technology can identify QD probes according to their spectral signatures and generate coexpression and stain titer maps by a clustering method. The experimental results show that the MHI method has potential to unmix biomarkers by their spectral information, which opens up a pathway of optical multiplexing with many different QD probes.
Hyperspectral blood image has been utilized in biomedical field for a period of time. However, identifying and segmenting blood cells is still a tricky issue. Thus, this paper proposed a new method based on support vector machine (SVM) to solve this issue from hyperspectral images. Then post-processing of holes-filling and noise removing are performed on the segmented results to get completed cell. The experimental results proved the accuracy and accommodation for this new proposed method.
Spectral imaging is a technology that integrates conventional imaging and spectroscopy to get both spatial and spectral information from an object. Although this technology was originally developed for remote sensing, it has been extended to the biomedical engineering field as a powerful analytical tool for biological and biomedical research. This review introduces the basics of spectral imaging, imaging methods, current equipment, and recent advances in biomedical applications. The performance and analytical capabilities of spectral imaging systems for biological and biomedical imaging are discussed. In particular, the current achievements and limitations of this technology in biomedical engineering are presented. The benefits and development trends of biomedical spectral imaging are highlighted to provide the reader with an insight into the current technological advances and its potential for biomedical research.
White blood cells (WBC) are comparatively significant components in the human blood system, and they have a pathological relationship with some blood-related diseases. To analyze the disease information accurately, the most essential work is to segment WBCs. We propose a new method for pathological WBC segmentation based on a hyperspectral imaging system. This imaging system is used to capture WBC images, which is characterized by acquiring 1-D spectral information and 2-D spatial information for each pixel. A spectral information divergence algorithm is presented to segment pathological WBCs into four parts. In order to evaluate the performance of the new approach, K-means and spectral angle mapper-based segmental methods are tested in contrast on six groups of blood smears. Experimental results show that the presented method can segment pathological WBCs more accurately, regardless of their irregular shapes, sizes, and gray-values.
A molecular spectral imaging system instead of common microscope was used to capture the spectral
images of blood smears. Then an improved spectral angle mapper algorithm for automatic blood cells
segmentation was presented. In this algorithm, the spectral vectors of blood cells were normalized first.
Then the spectral angles of all bands and partial bands were calculated respectively. Finally, the blood
cells were segmented according to the spectral angles combined with the threshold segmentation
method. As a case study, the leukemia cells were selected as the target and segmented with the new
algorithm. The results demonstrate that this algorithm can utilizes both spectral and spatial information
of blood cells and segment the leukemia cells more accurately.
To aid ophthalmologists in determining the pathogenesis of diabetic retinopathy and in evaluating the effects of medication, a microscopic pushbroom hyperspectral imaging system is developed. 40 healthy Wistar rats of half gender are selected in this study. They are divided into three groups (six rats failed to be models). 10 normal rats as the normal control group, 12 diabetic rats without any treatment as the model control group, and another 12 diabetic rats treated with LCVS1001 as the LCVS1001 group. The microscopic hyperspectral image of each retina section is collected and processed. Some typical spectrum curves between 400 and 800 nm of the outer nuclear layer are extracted, and images at various wavelengths are analyzed. The results show that a small trough appears near 522.2 nm in the typical spectrum curve of the model control group, and the transmittance of it is higher than that of the normal control group. In addition, the spectrum of the LCVS1001 group changes gradually to the normal spectrum after treatment with LCVS1001. Our findings indicate that LCVS1001 has some therapeutic effect on the diabetic retinopathy of rats, and the microscopic pushbroom hyperspectral imaging system can be used to study the pathogenesis of diabetic retinopathy.
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