Morphological Feature Extraction from Low Quality of Thick Blood Smear Microphotographs

Although the Giemsa-stained thick blood smear (GTS) remains the gold standard for the diagnosis of malaria, molecular methods are more sensitive and specific to detect parasites and can be used at reference centers to evaluate the performance of microscopy. The description of the Plasmodium falciparum, P. vivax, P. malariae and P. ovale ssrRNA gene sequences allowed the development of a polymerase chain reaction (PCR) that had been used to differentiate the four species. The objective of this study was to determine Plasmodium species through PCR in 190 positive smears from patients in order to verify the quality of diagnosis at SUCEN's Malaria Laboratory. Considering only the 131 positive results in both techniques, GTS detected 4.6% of mixed and 3.1% of P. malariae infections whereas PCR identified 19.1% and 13.8%, respectively.

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Instrumental Odour Monitoring System Classification Performance Optimization by Analysis of Different Pattern-Recognition and Feature Extraction Techniques

Sensors ◽

10.3390/s21010114 ◽

2020 ◽

Vol 21 (1) ◽

pp. 114

Author(s):

Tiziano Zarra ◽

Mark Gino K. Galang ◽

Florencio C. Ballesteros ◽

Vincenzo Belgiorno ◽

Vincenzo Naddeo

Keyword(s):

Pattern Recognition ◽

Feature Extraction ◽

Performance Optimization ◽

Industrial Plant ◽

Mathematical Treatment ◽

Extraction Techniques ◽

Peak Period ◽

Linear Discriminant ◽

Artificial Neural

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

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Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽

10.3390/s19040916 ◽

2019 ◽

Vol 19 (4) ◽

pp. 916 ◽

Cited By ~ 2

Author(s):

Wen Cao ◽

Chunmei Liu ◽

Pengfei Jia

Keyword(s):

Feature Extraction ◽

Kernel Function ◽

Electronic Nose ◽

Classification Accuracy ◽

Extraction Methods ◽

Object Function ◽

Optimal Value ◽

Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

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Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models

BMC Bioinformatics ◽

10.1186/s12859-021-04036-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Fetulhak Abdurahman ◽

Kinde Anlay Fante ◽

Mohammed Aliy

Keyword(s):

Object Detection ◽

Malaria Parasite ◽

Blood Smear ◽

Clustering Algorithm ◽

State Of The Art ◽

Detection Accuracy ◽

Small Object ◽

Thick Blood Smear ◽

Malaria Parasites ◽

Microscopic Images

Abstract Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

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Disseminated toxoplasmosis in an immunocompetent patient from Peruvian Amazon

Revista do Instituto de Medicina Tropical de São Paulo ◽

10.1590/s0036-46652010000200008 ◽

2010 ◽

Vol 52 (2) ◽

pp. 107-110 ◽

Cited By ~ 18

Author(s):

Juan Nunura ◽

Tania Vásquez ◽

Sergio Endo ◽

Daniela Salazar ◽

Alejandrina Rodriguez ◽

...

Keyword(s):

Visual Acuity ◽

Case Report ◽

Muscle Biopsy ◽

Blood Smear ◽

Immunocompetent Patient ◽

Peruvian Amazon ◽

Thick Blood Smear ◽

First Case ◽

Hip Extension

We report a case of severe toxoplasmosis in an immunocompetent patient, characterized by pneumonia, retinochoroiditis, hepatitis and myositis. Diagnosis was confirmed by serology, T. gondii in thick blood smear and presence of bradyzoites in muscle biopsy. Treatment with pyrimethamine plus sulfadoxine was successful but visual acuity and hip extension were partially recovered. This is the first case report of severe toxoplasmosis in an immunocompetent patient from Peru.

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On Directionality in Morphological Feature Extraction

Computer Vision and Graphics - Lecture Notes in Computer Science ◽

10.1007/978-3-642-33564-8_81 ◽

2012 ◽

pp. 677-684

Author(s):

Michał Świercz ◽

Marcin Iwanowski

Keyword(s):

Feature Extraction ◽

Morphological Feature

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An Efficient Adaptive Algorithm for Electron Microscopic Image Enhancement and Feature Extraction

International Journal of Computer Vision and Image Processing ◽

10.4018/ijcvip.2019010101 ◽

2019 ◽

Vol 9 (1) ◽

pp. 1-16

Author(s):

Vivek Arya ◽

Vipul Sharma ◽

Garima Arya

Keyword(s):

Feature Extraction ◽

Contrast Enhancement ◽

Block Size ◽

Sigmoid Function ◽

Microscopic Image ◽

Electron Microscopic ◽

Microscopic Images ◽

Electron Microscopic Image ◽

Block Based

In this article, a block-based adaptive contrast enhancement algorithm has been proposed, which uses a modified sigmoid function for the enhancement and features extraction of electron microscopic images. The algorithm is based on a modified sigmoid function that adapts according to the input microscopic image statistics. For feature extraction, the contrast of the image is very important and authentic property by which this article enhances the visual quality of the image. In this work, for better contrast enhancement of image, a block based on input value, combined with a modified sigmoid function that is used as contrast enhancer provides better EMF values for a smaller block size. It provides localized contrast enhancement effects adaptively which is not possible using other existing techniques. Simulation and experimental results demonstrate that the proposed technique gives better results compared to other existing techniques when applied to electron microscopic images. After the enhancement of microscopic images of actinomycetes, various important features are shown, like coil or spiral, long filament, spore and rod shape structures. The proposed algorithm works efficiently for different dark and bright microscopic images.

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