Blood smear test is the basic method of blood cytology and is also a standard medical test that can help diagnose various conditions and diseases. Morphological examination is the gold standard to determine pathological changes in blood cell morphology. In the biology and medicine automation trend, blood smears' automated management and analysis is very necessary. An online blood smear automatic microscopic image detection system has been constructed. It includes an online blood smear automatic producing part and a blood smear automatic microscopic image detection part. Online identity authentication is at the core of the system. The identifiers printed online always present dot matrix digit code (DMDC) whose stroke is not continuous. Considering the particularities of DMDC and the complexities of online application environment, an online identity authentication method for blood smear with heterological theory is proposed. By synthesizing the certain regional features according to the heterological theory, high identification accuracy and high speed have been guaranteed with few features required. In the experiment, the sufficient correct matches between the tube barcode and the identification result verified its feasibility and validity.

Near infrared chemical imaging (NIR-CI) combines conventional near infrared (NIR) spectroscopy with chemical imaging, thus provides spectral and spatial information simultaneously. It could be utilized to visualize the spatial distribution of the ingredients in a sample. The data acquired using NIR-CI instrument are hyperspectral data cube (hypercube) containing thousands of spectra. Chemometric methodologies are necessary to transform spectral information into chemical information. Partial least squares (PLS) method was performed to extract chemical information of chlorpheniramine maleate in pharmaceutical formulations. A series of samples which consisted of different CPM concentrations (w/w) were compressed and hypercube data were measured. The spectra extracted from the hypercube were used to establish the PLS model of CPM. The results of the model were R²_val0.981, RMSEC 0.384%, RMSECV 0.483%, RMSEP 0.631%, indicating that this model was reliable.

In this research, a new method based on the hyperspectral imaging for searching the best decocting time of sun dried ginseng is reported. The spectral images at different decocting time of test sample have been taken by the staring hyperspectral fluorescence imaging system and the solubility of active ingredients have been discussed by analyzing the changes on the spectral curves. The spectral range of the system is 400–720 nm and the spectral resolution is 5 nm. In the decocting process, the active ingredients of nonsoaked ginseng was dissolved in the tissue fluid at first, and reached equilibrium condition at last after the precipitation–dissolution reciprocating process of boiling. At last, the experimental results show that the best decoction time of sun dried ginseng is about 60 min after boiling.

Pulsed and continuous-wave (CW) lasers have been widely used as the light sources for photodynamic therapy (PDT) treatment. Singlet oxygen (¹O₂) is known to be a major cytotoxic agent in type-II PDT and can be directly detected by its near-infrared luminescence at 1270 nm. As compared to CW laser excitation, the effects of pulse width and repetition rate of pulsed laser on the kinetics and production of ¹O₂ luminescence were quantitatively studied during photosensitization of Rose Bengal. Significant difference in kinetics of ¹O₂ luminescence was found under the excitation with various pulse widths of nanosecond, microsecond and CW irradiation with power of 20mW. The peak intensity and duration of ¹O₂ production varied with the pulse widths for pulsed laser excitation, while the ¹O₂ was generated continuously and its production reached a steady state with CW excitation. However, no significant difference (P > 0:05) in integral ¹O₂ production was observed. The results suggest that the PDT efficacy using pulsed laser may be identical to the CW laser with the same wavelength and the same average fluence rate below a threshold in solution.

Recently, automatic diagnosis of diabetic retinopathy (DR) from the retinal image is the most significant research topic in the medical applications. Diabetic macular edema (DME) is the major reason for the loss of vision in patients suffering from DR. Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities. Many techniques are developed to diagnose the DR. The major drawbacks of the existing techniques are low accuracy and high time complexity. To overcome these issues, this paper proposes an enhanced particle swarm optimization-differential evolution feature selection (PSO-DEFS) based feature selection approach with biometric authentication for the identification of DR. Initially, a hybrid median filter (HMF) is used for pre-processing the input images. Then, the pre-processed images are embedded with each other by using least significant bit (LSB) for authentication purpose. Simultaneously, the image features are extracted using convoluted local tetra pattern (CLTrP) and Tamura features. Feature selection is performed using PSO-DEFS and PSO-gravitational search algorithm (PSO-GSA) to reduce time complexity. Based on some performance metrics, the PSODEFS is chosen as a better choice for feature selection. The feature selection is performed based on the fitness value. A multi-relevance vector machine (M-RVM) is introduced to classify the 13 normal and 62 abnormal images among 75 images from 60 patients. Finally, the DR patients are further classified by M-RVM. The experimental results exhibit that the proposed approach achieves better accuracy, sensitivity, and specificity than the existing techniques.

A leukocyte image fast scanning method based on max-min distance clustering is proposed. Because of the lower proportion and uneven distribution of leukocytes in human peripheral blood, there will not be any leukocyte in lager quantity of the captured images if we directly scan the blood smear along an ordinary zigzag scanning routine with high power (100^x) objective. Due to the larger field of view of low power (10^x) objective, the captured low power blood smear images can be used to locate leukocytes. All of the located positions make up a specific routine, if we scan the blood smear along this routine with high power objective, there will be definitely leukocytes in almost all of the captured images. Considering the number of captured images is still large and some leukocytes may be redundantly captured twice or more, a leukocyte clustering method based on max–min distance clustering is developed to reduce the total number of captured images as well as the number of redundantly captured leukocytes. This method can improve the scanning efficiency obviously. The experimental results show that the proposed method can shorten scanning time from 8.0–14.0 min to 2.5–4.0 min while extracting 110 nonredundant individual high power leukocyte images.

People's working capability is badly affected when they suffer an amputated arm. Artificial replacements with prosthetic devices to get a satisfactory level of performance for essential functions with the currently available prosthetic technology are very difficult. Myoelectric arm prostheses are becoming popular because they are operated by a natural contraction of intact muscles. Hence, SEMG based artificial arm was fabricated. The system consists of different electronic and mechanical assemblies for operation of hand utilizing microcontroller in order to have minimum signal loss during its processing. With the help of relay switching connected to low power DC motor, system is capable of opening and closing of grip according to individual wish.

Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically SP₃, is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP (StOMP), and regularized OMP (ROMP). The CoSaMP combined with SP₃ shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

The objective of the work is to investigate the classification of different movements based on the surface electromyogram (SEMG) pattern recognition method. The testing was conducted for four arm movements using several experiments with artificial neural network classification scheme. Six time domain features were extracted and consequently classification was implemented using back propagation neural classifier (BPNC). Further, the realization of projected network was verified using cross validation (CV) process; hence ANOVA algorithm was carried out. Performance of the network is analyzed by considering mean square error (MSE) value. A comparison was performed between the extracted features and back propagation network results reported in the literature. The concurrent result indicates the significance of proposed network with classification accuracy (CA) of 100% recorded from two channels, while analysis of variance technique helps in investigating the effectiveness of classified signal for recognition tasks.