56 research outputs found
Fuzzy technique for microcalcifications clustering in digital mammograms
Background
Mammography has established itself as the most efficient technique for the identification of the pathological breast lesions. Among the various types of lesions, microcalcifications are the most difficult to identify since they are quite small (0.1-1.0 mm) and often poorly contrasted against an images background. Within this context, the Computer Aided Detection (CAD) systems could turn out to be very useful in breast cancer control.
Methods
In this paper we present a potentially powerful microcalcifications cluster enhancement method applicable to digital mammograms. The segmentation phase employs a form filter, obtained from LoG filter, to overcome the dependence from target dimensions and to optimize the recognition efficiency. A clustering method, based on a Fuzzy C-means (FCM), has been developed. The described method, Fuzzy C-means with Features (FCM-WF), was tested on simulated clusters of microcalcifications, implying that the location of the cluster within the breast and the exact number of microcalcifications are known.The proposed method has been also tested on a set of images from the mini-Mammographic database provided by Mammographic Image Analysis Society (MIAS) publicly available.
Results
The comparison between FCM-WF and standard FCM algorithms, applied on both databases, shows that the former produces better microcalcifications associations for clustering than the latter: with respect to the private and the public database we had a performance improvement of 10% and 5% with regard to the Merit Figure and a 22% and a 10% of reduction of false positives potentially identified in the images, both to the benefit of the FCM-WF. The method was also evaluated in terms of Sensitivity (93% and 82%), Accuracy (95% and 94%), FP/image (4% for both database) and Precision (62% and 65%).
Conclusions
Thanks to the private database and to the informations contained in it regarding every single microcalcification, we tested the developed clustering method with great accuracy. In particular we verified that 70% of the injected clusters of the private database remained unaffected if the reconstruction is performed with the FCM-WF. Testing the method on the MIAS databases allowed also to verify the segmentation properties of the algorithm, showing that 80% of pathological clusters remained unaffected
HEp-2 Cell Classification with heterogeneous classes-processes based on K-Nearest Neighbours
We present a scheme for the feature extraction and classification of the fluorescence staining patterns of HEp-2 cells in IIF images. We propose a set of
complementary processes specific to each class of patterns to search. Our set of processes consists of preprocessing,features extraction and classification. The choice of methods, features and parameters was performed
automatically, using the Mean Class Accuracy (MCA) as a figure of merit. We extract a large number (108) of features able to fully characterize the staining pattern of HEp-2 cells. We propose a classification approach based
on two steps: the first step follows the one-against-all(OAA) scheme, while the second step follows the one-against-one (OAO) scheme. To do this, we needed to implement 21 KNN classifiers: 6 OAA and 15 OAO.
Leave-one-out image cross validation method was used for the evaluation of the results
Quantification and segmentation of breast cancer diagnosis: efficient hardware accelerator approach
The mammography image eccentric area is the breast density percentage
measurement. The technical challenge of quantification in radiology leads to
misinterpretation in screening. Data feedback from society, institutional, and industry
shows that quantification and segmentation frameworks have rapidly become the
primary methodologies for structuring and interpreting mammogram digital images.
Segmentation clustering algorithms have setbacks on overlapping clusters, proportion,
and multidimensional scaling to map and leverage the data. In combination,
mammogram quantification creates a long-standing focus area. The algorithm
proposed must reduce complexity and target data points distributed in iterative, and
boost cluster centroid merged into a single updating process to evade the large storage
requirement. The mammogram database's initial test segment is critical for evaluating
performance and determining the Area Under the Curve (AUC) to alias with medical
policy. In addition, a new image clustering algorithm anticipates the need for largescale
serial and parallel processing. There is no solution on the market, and it is
necessary to implement communication protocols between devices. Exploiting and
targeting utilization hardware tasks will further extend the prospect of improvement in
the cluster. Benchmarking their resources and performance is required. Finally, the
medical imperatives cluster was objectively validated using qualitative and
quantitative inspection. The proposed method should overcome the technical
challenges that radiologists face
Segmentation of turbulent computational fluid dynamics simulations with unsupervised ensemble learning
Computer vision and machine learning tools offer an exciting new way for automatically analyzing and categorizing information from complex computer simulations. Here we design an ensemble machine learning framework that can independently and robustly categorize and dissect simulation data output contents of turbulent flow patterns into distinct structure catalogs. The segmentation is performed using an unsupervised clustering algorithm, which segments physical structures by grouping together similar pixels in simulation images. The accuracy and robustness of the resulting segment region boundaries are enhanced by combining information from multiple simultaneously-evaluated clustering operations. The stacking of object segmentation evaluations is performed using image mask combination operations. This statistically-combined ensemble (SCE) of different cluster masks allows us to construct cluster reliability metrics for each pixel and for the associated segments without any prior user input. By comparing the similarity of different cluster occurrences in the ensemble, we can also assess the optimal number of clusters needed to describe the data. Furthermore, by relying on ensemble-averaged spatial segment region boundaries, the SCE method enables reconstruction of more accurate and robust region of interest (ROI) boundaries for the different image data clusters. We apply the SCE algorithm to 2-dimensional simulation data snapshots of magnetically-dominated fully-kinetic turbulent plasma flows where accurate ROI boundaries are needed for geometrical measurements of intermittent flow structures known as current sheets.Peer reviewe
Preliminary results of the project A.I.D.A. (Auto Immunity: Diagnosis Assisted by computer)
In this paper, are presented the preliminary results of the A.I.D.A. (Auto Immunity: Diagnosis
Assisted by computer) project which is developed in the frame of the cross-border cooperation Italy-Tunisia.
According to the main objectives of this project, a database of interpreted Indirect ImmunoFluorescence (IIF)
images on HEp 2 cells is being collected thanks to the contribution of Italian and Tunisian experts involved in
routine diagnosis of autoimmune diseases. Through exchanging images and double reporting; a Gold Standard
database, containing around 1000 double reported IIF images with different patterns including negative tests,
has been settled. This Gold Standard database has been used for optimization of a computing solution (CADComputer
Aided Detection) and for assessment of its added value in order to be used along with an
immunologist as a second reader in detection of auto antibodies for autoimmune disease diagnosis. From the
preliminary results obtained, the CAD appeared more powerful than junior immunologists used as second
readers and may significantly improve their efficacy
Optimized Swarm Enabled Deep Learning Technique for Bone Tumor Detection using Histopathological Image
Cancer subjugates a community that lacks proper care. It remains apparent that research studies enhance novel benchmarks in developing a computer-assisted tool for prognosis in radiology yet an indication of illness detection should be recognized by the pathologist. In bone cancer (BC), Identification of malignancy out of the BC’s histopathological image (HI) remains difficult because of the intricate structure of the bone tissue (BTe) specimen. This study proffers a new approach to diagnosing BC by feature extraction alongside classification employing deep learning frameworks. In this, the input is processed and segmented by Tsallis Entropy for noise elimination, image rescaling, and smoothening. The features are excerpted employing Efficient Net-based Convolutional Neural Network (CNN) Feature Extraction. ROI extraction will be employed to enhance the precise detection of atypical portions surrounding the affected area. Next, for classifying the accurate spotting and for grading the BTe as typical and a typical employing augmented XGBoost alongside Whale optimization (WOA). HIs gathering out of prevailing scales patients is acquired alongside texture characteristics of such images remaining employed for training and testing the Neural Network (NN). These classification outcomes exhibit that NN possesses a hit ratio of 99.48 percent while this occurs in BT classification
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions
Breast cancer has reached the highest incidence rate worldwide among all
malignancies since 2020. Breast imaging plays a significant role in early
diagnosis and intervention to improve the outcome of breast cancer patients. In
the past decade, deep learning has shown remarkable progress in breast cancer
imaging analysis, holding great promise in interpreting the rich information
and complex context of breast imaging modalities. Considering the rapid
improvement in the deep learning technology and the increasing severity of
breast cancer, it is critical to summarize past progress and identify future
challenges to be addressed. In this paper, we provide an extensive survey of
deep learning-based breast cancer imaging research, covering studies on
mammogram, ultrasound, magnetic resonance imaging, and digital pathology images
over the past decade. The major deep learning methods, publicly available
datasets, and applications on imaging-based screening, diagnosis, treatment
response prediction, and prognosis are described in detail. Drawn from the
findings of this survey, we present a comprehensive discussion of the
challenges and potential avenues for future research in deep learning-based
breast cancer imaging.Comment: Survey, 41 page
Designing a secure ubiquitous mammography consultation system
This thesis attempts to design and develop a prototype for mammography image
consultation that can work securely within a ubiquitous environment. Mammogram images
differ largely from other type of images and it requires special and dedicated techniques to
identify the required regions of interest. Thus in Chapter 2 we started to explore the
affectivity of the various traditional techniques based on convolution operators (e.g. Sobol,
Pretwitt, Canny) for mammography edge detection. The second part of chapter 2 tries to
enhance the results obtained via the traditional techniques by hybriding some of them. The
hybriding technique is called in our thesis as Pipelined Operators. In this direction we
proposed four pipeline operators, which contribute to the edge enhancement as well as
abnormalities rendering through the introduction of an additional coloring mechanism.
Although the visualization pipelines represent in our view an advancement on the
traditional techniques applied to mammograms, such pipelines expose healthcare users to
further usage complexities. For this purpose we extended our research work in chapter 2 to
find a better single technique that can work smoothly within the healthcare system. In this
direction, we developed in the third part of chapter 2 a novel technique for finding edges
based on analyzing the dynamic and fuzzy nature of edges in mammograms. We called our
developed method as "Dynamic Fuzzy Classifier or the DFC"
Machine learning methods for the characterization and classification of complex data
This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografÃa de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalÃas que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topologÃa de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatÃa diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anà lisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automà tic sense supervisió que ordena
imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automà tica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà , sent útil, fins i tot, per a la detecció automà tica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un là ser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat.
Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version
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