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1.
J Med Imaging (Bellingham) ; 5(1): 014008, 2018 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-29651450

RESUMO

A method is presented to automatically track and segment pelvic organs on dynamic magnetic resonance imaging (MRI) followed by multiple-object trajectory classification to improve understanding of pelvic organ prolapse (POP). POP is a major health problem in women where pelvic floor organs fall from their normal position and bulge into the vagina. Dynamic MRI is presently used to analyze the organs' movements, providing complementary support for clinical examination. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In the proposed method, organs are first tracked and segmented using particle filters and [Formula: see text]-means clustering with prior information. Then, the trajectories of the pelvic organs are modeled using a coupled switched hidden Markov model to classify the severity of POP. Results demonstrate that the presented method can automatically track and segment pelvic organs with a Dice similarity index above 78% and Hausdorff distance of [Formula: see text] for 94 tested cases while demonstrating correlation between organ movement and POP. This work aims to enable automatic tracking and analysis of multiple deformable structures from images to improve understanding of medical disorders.

2.
IEEE J Biomed Health Inform ; 20(1): 249-55, 2016 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-25438328

RESUMO

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures automatically. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines and nonlinear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (dice similarity index >0.75) in 87-91% of the images. This research aims to enable accurate, consistent, and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female POP.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Ossos Pélvicos/anatomia & histologia , Feminino , Humanos , Prolapso de Órgão Pélvico/diagnóstico , Prolapso de Órgão Pélvico/patologia , Máquina de Vetores de Suporte
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2016: 2403-2406, 2016 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-28268809

RESUMO

Pelvic organ prolapse is a major health problem in women where pelvic floor organs (bladder, uterus, small bowel, and rectum) fall from their normal position and bulge into the vagina. Dynamic Magnetic Resonance Imaging (DMRI) is presently used to analyze the organs' movements from rest to maximum strain providing complementary support for diagnosis. However, there is currently no automated or quantitative approach to measure the movement of the pelvic organs and their correlation with the severity of prolapse. In this paper, a two-stage method is presented to automatically track and segment pelvic organs on DMRI followed by a multiple-object trajectory classification method to improve the diagnosis of pelvic organ prolapse. Organs are first tracked using particle filters and K-means clustering with prior information. Then, they are segmented using the convex hull of the cluster of particles. Finally, the trajectories of the pelvic organs are modeled using a new Coupled Switched Hidden Markov Model (CSHMM) to classify the severity of pelvic organ prolapse. The tracking and segmentation results are validated using Dice Similarity Index (DSI) whereas the classification results are compared with two manual clinical measurements. Results demonstrate that the presented method is able to automatically track and segment pelvic organs with a DSI above 82% for 26 out of 46 cases and DSI above 75% for all 46 tested cases. The accuracy of the trajectory classification model is also better than current manual measurements.


Assuntos
Imageamento por Ressonância Magnética , Diafragma da Pelve/diagnóstico por imagem , Prolapso de Órgão Pélvico/diagnóstico por imagem , Algoritmos , Análise por Conglomerados , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Reconhecimento Automatizado de Padrão , Diafragma da Pelve/patologia , Prolapso de Órgão Pélvico/patologia , Reto , Reprodutibilidade dos Testes , Bexiga Urinária , Vagina
4.
IEEE J Biomed Health Inform ; 18(4): 1370-8, 2014 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-25014940

RESUMO

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Osso Púbico/anatomia & histologia , Algoritmos , Feminino , Humanos , Prolapso de Órgão Pélvico
5.
Artigo em Inglês | MEDLINE | ID: mdl-25570709

RESUMO

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are currently identified manually on MRI to identify reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures without any user interaction. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this research, we present a model that automatically identifies the bounding boxes of the bone structures on MRI using support vector machines (SVM) based classification and non-linear regression model that captures global and local information. Based on the relative locations of pelvic bones and organs, and local information such as texture features, the model identifies the location of the pelvic bone structures by establishing the association between their locations. Results show that the proposed method is able to locate the bone structures of interest accurately. The pubic bone, sacral promontory, and coccyx were correctly detected (DSI > 0.75) in 92%, 90%, and 88% of the testing images. This research aims to enable accurate, consistent and fully automated identification of pelvic bone structures on MRI to facilitate and improve the diagnosis of female pelvic organ prolapse.


Assuntos
Imageamento por Ressonância Magnética/métodos , Ossos Pélvicos/anatomia & histologia , Prolapso de Órgão Pélvico/diagnóstico , Máquina de Vetores de Suporte , Feminino , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Pelve/anatomia & histologia , Reto/anatomia & histologia , Análise de Regressão , Bexiga Urinária/anatomia & histologia
6.
IEEE Trans Pattern Anal Mach Intell ; 27(9): 1485-90, 2005 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-16173190

RESUMO

The technique of scale multiplication is analyzed in the framework of Canny edge detection. A scale multiplication function is defined as the product of the responses of the detection filter at two scales. Edge maps are constructed as the local maxima by thresholding the scale multiplication results. The detection and localization criteria of the scale multiplication are derived. At a small loss in the detection criterion, the localization criterion can be much improved by scale multiplication. The product of the two criteria for scale multiplication is greater than that for a single scale, which leads to better edge detection performance. Experimental results are presented.


Assuntos
Algoritmos , Inteligência Artificial , Aumento da Imagem/métodos , Interpretação de Imagem Assistida por Computador/métodos , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão/métodos , Armazenamento e Recuperação da Informação/métodos , Processamento de Sinais Assistido por Computador , Técnica de Subtração
7.
Biosens Bioelectron ; 19(8): 875-83, 2004 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-15128107

RESUMO

The development of a nanoparticle-based detection methodology for sensitive and specific DNA-based diagnostic applications is described. The technology utilizes gold nanoparticles derivatized with thiol modified oligonucleotides that are designed to bind complementary DNA targets. A glass surface with arrays of immobilized oligonucleotide capture sequences is used to capture DNA targets, which are then detected via hybridization to the gold nanoparticle probes. Amplification with silver allows for detection and quantitation by measuring evanescent wave induced light scatter with low-cost optical detection systems. Compared to Cy3-based fluorescence, silver amplified gold nanoparticle probes provide for a approximately 1000-fold increase in sensitivity. Furthermore, direct detection of non-amplified genomic DNA from infectious agents is afforded through increased specificity and even identification of single nucleotide polymorphisms (SNP) in human genomic DNA appears feasible.


Assuntos
Sondas de DNA/química , DNA/análise , Ouro/química , Microscopia de Fluorescência/métodos , Nanotubos/química , Nanotubos/ultraestrutura , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Proteínas de Bactérias/genética , DNA/química , DNA/ultraestrutura , Humanos , Interpretação de Imagem Assistida por Computador/métodos , Análise de Sequência com Séries de Oligonucleotídeos/instrumentação , Proteínas de Ligação às Penicilinas , Polimorfismo de Nucleotídeo Único/genética , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Prata/química , Coloração e Rotulagem , Staphylococcus aureus/genética
8.
IEEE Trans Med Imaging ; 22(9): 1089-99, 2003 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-12956264

RESUMO

Edge-preserving denoising is of great interest in medical image processing. This paper presents a wavelet-based multiscale products thresholding scheme for noise suppression of magnetic resonance images. A Canny edge detector-like dyadic wavelet transform is employed. This results in the significant features in images evolving with high magnitude across wavelet scales, while noise decays rapidly. To exploit the wavelet interscale dependencies we multiply the adjacent wavelet subbands to enhance edge structures while weakening noise. In the multiscale products, edges can be effectively distinguished from noise. Thereafter, an adaptive threshold is calculated and imposed on the products, instead of on the wavelet coefficients, to identify important features. Experiments show that the proposed scheme better suppresses noise and preserves edges than other wavelet-thresholding denoising methods.


Assuntos
Artefatos , Aumento da Imagem/métodos , Fígado/anatomia & histologia , Imageamento por Ressonância Magnética/métodos , Processamento de Sinais Assistido por Computador , Coluna Vertebral/anatomia & histologia , Processos Estocásticos , Algoritmos , Retroalimentação , Humanos , Análise Multivariada
9.
Anal Chem ; 74(8): 1792-7, 2002 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-11985309

RESUMO

The application of resonance light scattering (RLS) particles for high-sensitivity detection of DNA hybridization on cDNA microarrays is demonstrated. Arrays composed of approximately 2000 human genes ("targets") were hybridized with colabeled (Cy3 and biotin) human lung cDNA probes at concentrations ranging from 8.3 ng/microL to 16.7 pg/microL. After hybridization, the arrays were imaged using a fluorescence scanner. The arrays were then treated with 80-nm-diameter gold RLS Particles coated with anti-biotin antibodies and imaged in a white light, CCD-based imaging system. At low probe concentrations, significantly more genes were detected by RLS compared to labeling by Cy3. For example, for hybridizations with a probe concentration of 83.3 pg/microL, approximately 1150 positive genes were detected using RLS compared to approximately 110 positive genes detected with Cy3. In a differential gene expression experiment using human lung and leukemia RNA samples, similar differential expression profiles were obtained for labeling by RLS and fluorescence technologies. The use of RLS Particles is particularly attractive for detection and identification of low-abundance mRNAs and for those applications in which the amount of sample is limited.


Assuntos
Hibridização de Ácido Nucleico/métodos , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Sondas de DNA/química , Perfilação da Expressão Gênica/métodos , Humanos , Luz , Pulmão/química , RNA Neoplásico/análise , Espalhamento de Radiação , Sensibilidade e Especificidade
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