Abstract:Microscopic examination is one of the most common methods for acute lymphoblastic leukemia (ALL) diagnosis. Most traditional methods of automized blood cell identification are based on RGB color or gray images captured by light microscopes. This paper presents an identification method combining both spectral and spatial features to identify lymphoblasts from lymphocytes in hyperspectral images. Normalization and encoding method is applied for spectral feature extraction and the support vector machinerecursive feature elimination (SVM-RFE) algorithm is presented for spatial feature determination. A marker-based learning vector quantization (MLVQ) neural network is proposed to perform identification with the integrated features. Experimental results show that this algorithm yields identification accuracy, sensitivity, and specificity of 92.9%, 93.3%, and 92.5%, respectively. Hyperspectral microscopic blood imaging combined with neural network identification technique has the potential to provide a feasible tool for ALL prediagnosis. 3996-4009 (2015). 11. P. Froom, R. Havis, and M. Barak, "The rate of manual peripheral blood smear reviews in outpatients," Clin.

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.

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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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