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Qun Liu

Bio: Qun Liu is an academic researcher from South China University of Technology. The author has contributed to research in topics: Active contour model & Image segmentation. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Proceedings ArticleDOI
01 Oct 2017
TL;DR: The segmentation results shown in the paper demonstrated that the combined image segmentation algorithms and image preprocessing methods can successfully segment the thyroid regions out of thyroid ultrasound images.
Abstract: Image segmentation for thyroid ultrasound images is a challenging task. As such, several image segmentation algorithms combined with different image preprocessing methods applied to thyroid ultrasound image segmentation are studied in this work. The image segmentation algorithms presented in this paper include edge detection, regional segmentation and active contour without edge algorithms. The image preprocessing methods presented in this paper include Butterworth low-pass filtering, Butterworth high-pass enhanced filtering, and adaptive weighted median filtering. In the experiments, the image segmentation algorithms and image preprocessing methods were combined to evaluate the segmentation results for thyroid ultrasound images. The segmentation results shown in the paper demonstrated that the combined image segmentation algorithms and image preprocessing methods can successfully segment the thyroid regions out of thyroid ultrasound images.

3 citations


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Journal ArticleDOI
TL;DR: In this paper, a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears is presented, which is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself.
Abstract: Malaria is an infectious disease caused by Plasmodium parasites and is potentially human life-threatening. Children under 5 years old are the most vulnerable group with approximately one death every two minutes, accounting for more than 65% of all malaria deaths. The World Health Organization (WHO) encourages the research of appropriate methods to treat malaria through rapid and economical diagnostic. In this paper, we present a deep learning-based framework for diagnosing human malaria infection from microscopic images of thin blood smears. The framework is based on a direct segmentation and classification approach which relies on the analysis of the parasite itself. The framework permits to segment the Plasmodium parasite in the images and to predict its species among four dominant classes: P. Falciparum, P. Malaria, P. Ovale, and P. Vivax. A high potential of generalization with a competitive performance of our framework on inter-class data is demonstrated through an experimental study considering several datasets. Our source code is publicly available on https://github.com/Benhabiles-JUNIA/MalariaNet.

2 citations

04 Feb 2020
TL;DR: By implementing this system, the time for the diagnosis of malaria will be cut down, which will save lives and the medical resources that are used while waiting for the results of the tests from the old system.
Abstract: As technology has evolved it has become more and more efficient to diagnose, and treat multiple diseases. Malaria is one of the deadliest diseases on this pl anet. Each year it estimated that 1 million people die as a re sult of this disease. Furthermore 3.4 billion people are in dang er of contracting malaria. With advances in the field of medicine it is now entirely possible to not only tr eat but also prevent malaria. The way in which people are diagnosed for malaria today is through blood sample s. The techniques used currently are accurate however they are time consuming. This has necessitated doctors to st art the treatment for malaria before the blood work is finished, since in its later stage’s malaria can be very diff icult to cure. The system that is discussed aims to cut this time requirement by at least half and increase the accur acy of the tests. An automated system that gathers the ima ge data and analyses the images for malarial parasites is described. A system for collection of data and anal ysis is described. By implementing this system, the time ne eded for the diagnosis of malaria will be cut down. This will save lives and the medical resources that are used while waiting for the results of the tests from the old system. Keywords—Deep Learning, Malaria, Parasite

1 citations

Book ChapterDOI
16 May 2018
TL;DR: In this paper, the performance of image enhancement techniques on ultrasound images are evaluated using quality metrics, namely Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR).
Abstract: Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. To precisely diagnose the carotid plaque, the affected region should be segmented from the ultrasonic image of carotid artery. Many techniques have been used to identify the plaque in ultrasound images. Image enhancement and restoration are the important processes to acquire high-quality images from the noisy images. When the artery images are captured, noise occurs due to high-frequency rate. To acquire a high-quality image, preprocessing is the first step to be done. The quality of the image is improved in this process. The techniques involved in preprocessing are dealt in this paper. Preprocessing involves filtering the image and removing the noise by various filtering techniques. Salt-and-pepper and Gaussian noise in ultrasound images can be filtered using techniques like mean, median and Wiener filters. Salt-and-pepper noise is multiplicative in nature and it is introduced by the image acquisition mechanism. The quality of the input sensor is reflected by the Gaussian noise. In this paper, the performance of image enhancement techniques on ultrasound images are evaluated using quality metrics, namely Mean Square Error (MSE) and Peak Signal–to-Noise Ratio (PSNR).

1 citations