149 research outputs found
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Algorithmic processing to aid in leukemia detection
Get PDFBackground: I present our medical context with some basic concepts in order to understand the results of our work, and then I begin the explanation of mathematical morphology. I will conclude by the description of algorithmic processing propose in this paper. Cancers, including leukemia and lymphoma, can cause uncontrolled growth of an abnormal type of blood cell in the bone marrow, resulting in a greatly increased risk for infection and or serious bleeding.
Methods: We present detailed steps of our proposed systems, to obtain a final result that shows the detection of abnormal cells. It typically starts with a median filter pre-processing step and then applies different morphologic operator, which allows us to segment the original image and detect cancerous cells. The basic idea behind all the operators in the mathematical morphology is to compare the set of objects to analyze another object of known form, which is called a structuring element. The structuring element is a geometric figure, simple to form, known or arbitrary, and can be a circle, segment, square, or triangle.
Results: We show the different results obtained after testing carried out in algorithmic processing using MATLAB: To ameliorate the visualization of the abnormal blood cells, we have applied the elements basis morphological operations in a different way. We have performed an opening by reconstruction and a closing by reconstruction. The obtained result show that we have obtained an efficient detection of the targeted objects (abnormal blood cells or leukemia).
Conclusion: In this paper, we have utilized the operators of the mathematical morphology with the aim to detect abnormal cells for diagnostic aid and transmission of accurate and precise clinical information, which helps specialists in medicine (hematologists) to distinguish abnormal cells or cancerous and to follow the evolution of leukemia. The algorithmic processing presented in this article has been able to perform the task of detection of cancerous cells with success; it has produced remarkable and satisfactory results. We think of the future concept as a system of aid for diagnosis from microelectronics integration to the base of reconfigurable technologies applied to cells for the goal of quantification of the cancer region
Semantic Segmentation of Medical Images with Deep Learning: Overview
Get PDFSemantic segmentation is one of the biggest challenging tasks in computer vision, especially in medical image analysis, it helps to locate and identify pathological structures automatically. It is an active research area. Continuously different techniques are proposed. Recently Deep Learning is the latest technique used intensively to improve the performance in medical image segmentation. For this reason, we present in this non-systematic review a preliminary description about semantic segmentation with deep learning and the most important steps to build a model that deal with this problem
Medical Technologies Journal: Re-launching the publication
Get PDFWelcome to the fifth volume of the international peer reviewed journal: Medical Technologies Journal. We are proud to announce, in this editorial, the re-launching of publication on the journal. The journal receives all medical domains: trials and, synthesis; findings and innovations. After a period of silence due to several technical problems principally the COVID19 crises, we publish, for our community, this issue. It contains four scientific articles one review in ophthalmology and three in medical technologies
Quantitative Analysis in Multimodality Imaging: Challenges and Opportunities
Get PDFThis talk reflects the tremendous ongoing interest in molecular and dual-modality imaging (PET/CT, SPECT/CT and PET/MR) as both clinical and research imaging modalities in the past decade. An overview of molecular multi-modality medical imaging instrumentation as well as simulation, reconstruction, quantification, and related image processing issues with special emphasis on quantitative analysis of nuclear medical images are presented. This tutorial aims to bring the biomedical image processing community a review on the state-of-the-art algorithms used and under development for accurate quantitative analysis in multimodality and multi-parametric molecular imaging and their validation mainly from the developer’s perspective with emphasis on image reconstruction and analysis techniques. It will inform the audience about a series of advanced development recently carried out at the PET instrumentation & Neuroimaging Lab of Geneva University Hospital and other active research groups. Current and prospective future applications of quantitative molecular imaging also are addressed, especially its use prior to therapy for dose distribution modeling and optimization of treatment volumes in external radiation therapy and patient-specific 3D dosimetry in targeted therapy toward the concept of image-guided radiation therapy.
 
An Optimized Medical Image Watermarking Approach for E-Health Applications
Get PDFBackground: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission.
Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities.
Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms.
Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities
Classification of histological images of thyroid nodules based on a combination of Deep Features and Machine Learning
Get PDFBackground: Thyroid nodules are a prevalent worldwide disease with complex pathological types. They can be classified as either benign or malignant. This paper presents a tool for automatically classifying histological images of thyroid nodules, with a focus on papillary carcinoma and follicular adenoma.
Methods: In this work, two pre-trained Convolutional Neural Network (CNN) architectures, VGG16 and VGG19, are used to extract deep features. Then, a principal component analysis was used to reduce the dimensionality of the vectors. Then, three machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, and Random Forest) were used for classification. These investigations were applied to our database collection,
Results: The proposed investigations have been applied to our private database collection with a total of 112 histological images. The highest results were obtained by the VGG16 transfer deep feature and the SVM classifier with an accuracy rate equal to 100%
Proposal for Medical Data Transmission in Healthcare Systems
Get PDFBackground: Information systems used in hospitals are slow and consume a lot of system memory, facilitating crashes, impacting patients seeking consultation face long waiting periods by a medical specialist; Still considering that exchange patient data and medical consultations in system interconnected between hospitals, for scheduling of consultations may become even more latent.Methods: Aiming to solve such problems, the present study implements modeling with discrete-event technology applied to a healthcare system, modulating the signal transmitted with the DQPSK format, through the simulation environment, the Simulink of the MATLAB software, improving the transmission of data, through a pre-coding process of bits adopting discrete events in the signal before modulation.Results: This study aims to increase the information capacity for healthcare systems, bringing a new approach for signal transmission, undertaken in the discrete domain employing the discrete entities in the bit generation process, this use being the differential applied on the bit itself, in the physical layer, showing better computational performance regarding memory utilization related to compression of information, showing an improvement of 101.52%.Conclusion: The proposal developed has the properties of improving the capacity of hospital services and can increase the performance of the communication between all medical devices, this positive impact is the result that the data stream will consume fewer communication resources
Acute Pancreatitis Between Practice and Recommendations
Get PDFBackground:Acute inflammation of the pancreas, acute pancreatitis (AP) shows an increasing incidence in the world as well as in Algeria. However, lethality and mortality are decreasing, mainly due to recommendations from Atlanta, the International Pancreatology Association and the American Pancreatology Association. The purpose of this work was to analyze the management of AP in the general surgery department of the UHC of Sidi Bel Abbes.
Method: In a retrospective study, 100 acute pancreatitis cases admitted to general surgery between January 2017 and December 2019 were analyzed.
The abdominal scanner was systematic at the admission. The rate of lipasemia, when greater than 3 times the normal one, allowed, in association with the evocative pain, to make the diagnosis. The severity of AP was assessed using the Balthazar score. An MRI was requested when the scanner was inconclusive, to look for an etiology and / or possible complications. 79 (79%) patients received broad-spectrum antibiotic therapy and diet was systematic.
Results: We have transmitted the recommendations of scholar societies to the various specialists and the management of AP is currently more unified and more effective. Scanner is no longer systematic in the early phase. Antibiotic therapy is no longer administered without biological evidence. Enteral nutrition is now possible.
Conclusion: We were able to reach the goal of reducing hospitalization and scanning requests. A further analysis of 100 other PA files is planned to confirm the obtained results
Hematology and Digital Image Processing: Watershed Transform-Based Methodology for Blood Cell Counting Using the WT-MO Algorithm
Get PDFBackground: Most diseases can be detected by routine examination, even if they are in the initial phase. Currently, one of the most requested medical laboratory tests is that which allows detecting from bacterial infections until leukemias. However, for less favored populations, this examination can be seen as having a high cost.
Methods: Thus, this study introduces an algorithm of segmentation of images capable of detecting and counting red blood cells and leukocytes present in digital images of blood smear. The methodology was named by WT-MO, once it relies on the concepts of Watershed Transform and Morphological Operations. The experiments were conducted in the MATLAB software simulation environment, where 25 images were used in order to evaluate the accuracy, processing time, and execution time of the WT-MO algorithm.
Results: The results show that the WT-MO methodology presents high accuracy, reaching 96% and 92% in the red blood cell and leukocyte counts, respectively; reliability and low processing time, reaching an average processing time and execution time, achieving from 0.74 to 2.17 seconds. Therefore, the WT-MO algorithm can be seen as the first step in making laboratory tests more accessible to populations in underdeveloped and developing countries.
Conclusion: The WT-MO methodology helps not only disadvantaged populations gain access to low-cost, high-reliability tests but also has excellent potential for use in laboratories in developed countries
Self-Organizing Algorithm for Massive Tractography Datasets Clustering with Outliers Elimination Based on Multiple Species Flocking Model
Get PDFBackground: The study of White Matter (WM) connectivity is of general interest in neuroscience, which is achieved by the analysis and clustering of the streamlines composed of the tractography dataset. The WM streamlines clustering is a challenge, because of the complexity and the vast size of the WM tractography dataset and its composition by various streamlines, in additionally to the presence of outliers.
Methods: Several WM clustering methods have been proposed in the literature to overcome these constraints. However, these methods stay statics. Once the clustering streamline is performed, it stays in this one. In this paper, we propose a new framework of distributed multiagent, improving, and adapting a bio-inspired model called Multiple Species Flocking (MSF) for WM streamlines clustering and automatic outlier elimination.
Results: The basic MSF rules are modified and adapted to perform streamlines clustering in higher dimensions. Specifically, each streamline is associated with a mobile agent and move onto a virtual space to form a group following the defined rules. Only the agents assigned to similar streamlines form a flock, whereas the agents assigned to dissimilar streamlines are sidelined and considered as outliers.
Conclusion: Swarm intelligence features of the approach, such as adaptivity, parallelism, dynamism, and decentralization, make our algorithm scalable to large datasets, very fast and accurate, which are confirmed by experimental results on synthetic and real datasets