Beat-to-beat fetal heart rate analysis using portable medical device and wavelet transformation technique.

Objective: Beat-to-beat tele-fetal monitoring and comparison with clinical data are studied with a wavelet transformation approach. Tele-fetal monitoring is a big progress toward a wearable medical device for pregnant women capable of obtaining prenatal care at home. Study Design: We apply a wavelet transformation algorithm for fetal cardiac monitoring using a portable fetal Doppler medical device. After an investigation of 85 different mother wavelets, a bio-orthogonal 2.2 mother wavelet in level 4 of decomposition is chosen. The efficiency of the proposed method is evaluated using two data sets including public and clinical. Results: From publicly available data on PhysioBank, and simultaneous clinical measurement, we prove that the comparison between obtained fetal heart rate by the algorithm and the baselines yields a promising accuracy beyond 95%. Conclusion: Finally, we conclude that the proposed algorithm would be a robust technique for any similar tele-fetal monitoring approach.

The Ellipselet Transform.

BACKGROUND: A fair amount of important objects in natural images have circular and elliptical shapes. For example, the nucleus of most of the biological cells is circular, and a number of parasites such as Oxyuris have elliptical shapes in microscopic images. Hence, atomic representations by two-dimensional (2D) basis functions based on circle and ellipse can be useful for processing these images. The first researches have been done in this domain by introducing circlet transform. METHODS: The main goal of this article is expanding the circlet to a new one with elliptical basis functions. RESULTS: In this article, we first introduce a new transform called ellipselet and then compare it with other X-let transforms including 2D-discrete wavelet transform, dual-tree complex wavelet, curvelet, contourlet, steerable pyramid, and circlet transform in the application of image denoising. CONCLUSION: Experimental results show that for noises under 30, the ellipselet is better than other geometrical X-lets in terms of Peak Signal to Noise Ratio, especially for Lena which contains more circular structures. However, for Barbara which has fine structures in its texture, it has worse results than dual-tree complex wavelet and steerable pyramid.

The Best Texture Features for Leukocytes Recognition.

BACKGROUND: Differential counting of white blood cells (WBCs or leukocytes) is a common task to diagnose many diseases such as leukemia, and infections. An accurate process for recognizing leukocytes is to evaluate a blood smear under a microscope by an expert. Since, this procedure is manual, time-consuming and tedious, making the procedure automatic would overcome these problems. In an automated CAD (Computer-Aided-Design) system for this purpose, a crucial module is leukocytes recognition. In this paper, we are looking for the best features in order to recognize five types of leukocytes (Monocyte, Lymphocyte, Neutrophil, Eosinophil and Basophil) from microscopic images of blood smear in an automated cell counting system. METHODS: In this work, we focus on the texture features and seven categories: GLCM features, Haralick features, Spectral texture features, Wavelet-based features, Gabor-based features, CoALBP and RICLBP are analyzed to find the best features for leukocytes detection. The best features of each category are selected using stepwise regression and finally three well-known classifiers called K-NN, LDA and NB are utilized for classification. RESULTS: The proposed system is tested on a self-provided dataset composed of 200 cell images. In our experiments, to evaluate the process, the accuracy of each leukocyte type and the mean accuracy are computed. RICLBP features achieved the best mean accuracy (85.53%) for LDA classifier. CONCLUSIONS: In our experiments, although the maximum mean accuracy (85.53%) went with RICLBP features, but the accuracies of all five leukocyte types weren't maximized for RICLBP features. This result directs us to design and develop a system based on multiple features and multiple classifiers to maximize the accuracies even for each individual cell type in our future work.

Automatic detection of micronuclei by cell microscopic image processing.

With the development and applications of ionizing radiation in medicine, the radiation effects on human health get more and more attention. Ionizing radiation can lead to various forms of cytogenetic damage, including increased frequencies of micronuclei (MNi) and chromosome abnormalities. The cytokinesis block micronucleus (CBMN) assay is widely used method for measuring MNi to determine chromosome mutations or genome instability in cultured human lymphocytes. The visual scoring of MNi is time-consuming and scorer fatigue can lead to inconsistency. In this work, we designed software for the scoring of in vitro CBMN assay for biomonitoring on Giemsa-stained slides that overcome many previous limitations. Automatic scoring proceeds in four stages as follows. First, overall segmentation of nuclei is done. Then, binucleated (BN) cells are detected. Next, the entire cell is estimated for each BN as it is assumed that there is no detectable cytoplasm. Finally, MNi are detected within each BN cell. The designed Software is even able to detect BN cells with vague cytoplasm and MNi in peripheral blood smear. Our system is tested on a self-provided dataset and is achieved high sensitivities of about 98% and 82% in recognizing BN cells and MNi, respectively. Moreover, in our study less than 1% false positives were observed that makes our system reliable for practical MNi scoring.

Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing.

BACKGROUND: Segmentation of leukocytes acts as the foundation for all automated image-based hematological disease recognition systems. Most of the time, hematologists are interested in evaluation of white blood cells only. Digital image processing techniques can help them in their analysis and diagnosis. MATERIALS AND METHODS: The main objective of this paper is to detect leukocytes from a blood smear microscopic image and segment them into their two dominant elements, nucleus and cytoplasm. The segmentation is conducted using two stages of applying K-means clustering. First, the nuclei are segmented using K-means clustering. Then, a proposed method based on region growing is applied to separate the connected nuclei. Next, the nuclei are subtracted from the original image. Finally, the cytoplasm is segmented using the second stage of K-means clustering. RESULTS: The results indicate that the proposed method is able to extract the nucleus and cytoplasm regions accurately and works well even though there is no significant contrast between the components in the image. CONCLUSIONS: In this paper, a method based on K-means clustering and region growing is proposed in order to detect leukocytes from a blood smear microscopic image and segment its components, the nucleus and the cytoplasm. As region growing step of the algorithm relies on the information of edges, it will not able to separate the connected nuclei more accurately in poor edges and it requires at least a weak edge to exist between the nuclei. The nucleus and cytoplasm segments of a leukocyte can be used for feature extraction and classification which leads to automated leukemia detection.