Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor

This paper presents a new class of local neighborhood based wavelet feature descriptor (LNWFD) for content based medical image retrieval (CBMIR). To retrieve images effectively from large medical databases is backbone of diagnosis. Existing wavelet transform based medical image retrieval methods suffer from high length feature vector with confined retrieval performance. Triplet half-band filter bank (THFB) enhanced the properties of wavelet filters using three kernels. The influence of THFB has employed in the proposed method. First, triplet half-band filter bank (THFB) is used for single level wavelet decomposition to obtain four sub-bands. Next, the relationship among wavelet coefficients is exploited at each sub-band using 3 × 3 neighborhood window to form LNWFD pattern. The novelty of the proposed descriptor lies in exploring relation between wavelet transform values of pixels rather than intensity values which gives more detail local information in wavelet sub-bands. Thus, proposed feature descriptor is robust against illumination. Manhattan distance is used to compute similarity between query feature vector and feature vector of database. The proposed method is tested for medical image retrieval using OASIS-MRI, NEMA-CT, and Emphysema-CT databases. The average retrieval precisions achieved are 71.45%, 99.51% of OASIS-MRI and NEMA-CT databases for top ten matches considered respectively and 55.51% of Emphysema-CT database for top 50 matches. The superiority in terms of performance of the proposed method is confirmed by the experimental results over the well-known existing descriptors.

Research on Image Retrieval Method of Wild Chinese Herbal Medicine Plants in Changbai Mountain / 中国中医药信息杂志

Objective To study the visual word bag based image retrieval method and apply it in the field of image retrieval of wild Chinese herbal medicine plants in Changbai Mountain.Methods SURF operator was used to extract visual features. Then sparse coding method was used to structure visual dictionary. The classifier was trained by combination of support vector machine (SVM) and approximate nearest neighbors (ANN) method.Results Totally 2500 photos of Chinese herbal medicine plants were chosen. When the visual word number was 500, the average retrieval time was 481 ms, and the average query accuracy was 88.95％.Conclusion The method can effectively improve the efficiency and accuracy of image retrieval, and has better robust.

Content Based Geographic Image Retrieval using Local Vector Pattern

ABSTRACT Large image archives formed by satellite remote sensing missions are getting an increasing valuable source of information in Geographic Information Systems (GIS). The need for retrieving a required image from a huge image database is increasing significantly for the purpose of analyzing resources in GIS. Content Based Geographic Image Retrieval (CBGIR) in the image processing field is the best solution to meet the requirement. In this work, we used Local Vector Pattern (LVP) to extract fine features present in the geographical image and retrieve the applicable images from a large remote sensing image database. The primary idea of our method is generating micro patterns of LVP by the vectors of each pixel that are constructed by calculating the values between the centre pixels and its neighbourhood pixels with various distances of different directions. Then the proposed method was designed for concatenating these vector patterns to produce more unique features of geographical images and comparing the results with Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Tetra Pattern (LTrP). Ultimately, the extensive analysis carried out on different geographical image collections proved that the proposed method achieves the improved classification accuracy and better retrieving results.

RECUPERACIÓN BASADA EN CONTENIDO DE IMÁGENES MICROSCÓPICAS DE CUELLO UTERINO INFECTADAS CON EL VIRUS DEL PAPILOMA HUMANO EMPLEANDO CARACTERÍSTICAS DE TEXTURA / CONTENT-BASED RETRIEVAL OF CERVICAL MICROSCOPIC IMAGES INFECTED WITH HUMAN PAPILLOMAVIRUS USING TEXTURAL FEATURES / RECUPERAÇÃO BASEADA NO CONTEÚDO DE IMAGENS MICROSCÓPICAS DE COLO DO ÚTERO INFECTADO COM VÍRUS DO PAPILOMA HUMANO, UTILIZANDO CARACTERÍSTICAS DE TEXTURA

Se presenta el proceso de caracterización implementado para la obtención de descriptores visuales que representan el contenido visual de imágenes digitales de biopsias de cuello uterino infectadas con el Virus del Papiloma Humano (VPH), en las que se capturan tejidos con lesiones conocidas como Condiloma Plano Viral. A partir de la construcción de una base de datos de imágenes de biopsias de cuello uterino y el análisis e implementación de técnicas de filtrado que resaltan la información relacionada a las texturas contenidas en los tejidos que captura cada imagen y de técnicas de extracción de características que describen el contenido de las imágenes; se propone un conjunto de características que describen el contenido de las imágenes a partir de modificaciones propias de la Transformada Discreta de Wavelets y el cálculo de la Matriz de Coocurrencia, donde este conjunto de características propuesto proporcionó un porcentaje promedio de recuperación del 80% en imágenes microscópicas de cuello uterino infectadas con el VPH, sobre las cuales no se conocen sistemas CBIR desarrollados. Finalmente, se determina el porcentaje de recuperación promedio a partir del uso de métricas de similaridad basadas en la norma LP.

The purpose of this work is to report the characterization process implemented to obtain visual descriptors representing visual content of digital images of cervical biopsies infected with Human Papilloma Virus (HPV). Positive biopsies with infected tissues present lesions known as Condyloma Plano Viral. A database of images of cervical biopsies was constructed in addition to the implementation of techniques that enhance the texture information and describe the content of images. This work proposed a set of features to describe the content of images from custom modifications of Discrete Wavelet Transform and the calculation of the Co-occurrence Matrix. This proposed feature set provided an average recovery rate of 80% in microscopic images of the cervix infected with HPV, from which CBIR systems have not been developed. Finally, this work determines the average recovery rate from the use of similarity metrics based on the standard LP.

Neste trabalho é apresentado o processo implementado de caracterização para a obtenção de descrições visuais que representam o conteúdo visual de imagens digitais de biópsias cervicais infectadas com Papilomavírus Humano (HPV), capturadas em lesões de tecidos conhecidas como Condiloma Plano Viral. A partir da construção de uma base de dados de imagens de biópsias do colo uterino, análise e implementação de técnicas de filtragem de características que descrevem o conteúdo das imagems, propõe-se um conjunto de características que descrevem o conteúdo das imagens a partir de modificações próprias da Transformada Discreta de Wavelets e o cálculo da Matriz de co-ocorrência, onde o conjunto de características propostas resultou numa porcentagem média de 80% de recuperação nas imagens microscópicas de colo uterino infectado com o VPH, sobre as quais não se percebe o desenvolvimento dos sistemas CBIR. Finalmente, a taxa de recuperação média foi determinada a partir da utilização de métricas de similaridade com base na indicação de LP.

Medical Image Retrieval: Past and Present / 대한의료정보학회지

With the widespread dissemination of picture archiving and communication systems (PACSs) in hospitals, the amount of imaging data is rapidly increasing. Effective image retrieval systems are required to manage these complex and large image databases. The authors reviewed the past development and the present state of medical image retrieval systems including text-based and content-based systems. In order to provide a more effective image retrieval service, the intelligent content-based retrieval systems combined with semantic systems are required.

A system for aceessing a collection of histology images using content-based strategess / Sistema para acceder una colección de imágenes histológicas mediante estrategias basadas en el contenido

Histology images are an important resource for research, education and medical practice. The availability of image collections with reference purposes is limited to printed formats such as books and specialized journals. When histology image sets are published in digital formats, they are composed of some tens of images that do not represent the wide diversity of biological structures that can be found in fundamental tissues. Making a complete histology image collection available to the general public having a great impact on research and education in different areas such as medicine, biology and natural sciences. This work presents the acquisition process of a histology image collection with 20,000 samples in digital format, from tissue processing to digital image capturing. The main purpose of collecting these images is to make them available as reference material to the academic comunity. In addition, this paper presents the design and architecture of a system to query and explore the image collection, using content-based image retrieval tools and text-based search on the annotations provided by experts. The system also offers novel image visualization methods to allow easy identification of interesting images among hundreds of possible pictures. The system has been developed using a service-oriented architecture and allows web-based access in http://www.informed.unal.edu.co.

Las imágenes histológicas son un importante recurso para la investigación, la educación y la práctica médica. La disponibilidad de imágenes individuales o colecciones de imágenes de referencia está limitada a formatos impresos como libros y revistas científicas. En aquellos casos en donde se publican conjuntos de imágenes digitales, éstos están compuestos por algunas cuantas decenas de imágenes que no representan la gran diversidad de estructuras biológicas que pueden encontrarse en los tejidos fundamentales. Contar con una completa colección de imágenes histológicas es de gran apoyo para los procesos de investigación y educación en diferentes áreas de la medicina, biología y ciencias. En este trabajo se presenta el proceso de adquisición de una colección de 20.000 imágenes histológicas en formato digital, desde la preparación y fijación de los tejidos hasta su digitalización bajo el microscopio, con el propósito de publicarlas como material de referencia para la comunidad académica en general. Además, se presenta el diseño y la arquitectura de un sistema para consultar y explorar la colección de imágenes, utilizando herramientas de búsqueda basadas en el contenido de las imágenes y en las anotaciones provistas por los expertos. El sistema también ofrece novedosos mecanismos de visualización de las imágenes, para facilitar la tarea de identificar las imágenes interesantes entre otros cientos posibles en la colección. El sistema fue desarrollado usando una arquitectura orientada a servicios y ofrece acceso a través de la Web en http://www.informed.unal.edu.co.

Grades computacionais na recuperação de imagens médicas baseada em conteúdo / Grid computing in the optimization of content-based medical images retrieval

OBJETIVO: Utilizar o poder de processamento da tecnologia de grades computacionais para viabilizar a utilização do algoritmo de medida de similaridade na recuperação de imagens baseada em conteúdo. MATERIAIS E MÉTODOS: A técnica de recuperação de imagens baseada em conteúdo é composta de duas etapas seqüenciais: análise de textura e algoritmo de medida de similaridade. Estas são aplicadas em imagens de joelho e cabeça, nas quais se avaliaram a eficiência em recuperar imagens do mesmo plano e a seqüência de aquisição em um banco de 2.400 imagens médicas para testar a capacidade de recuperação de imagens baseada em conteúdo. A análise de textura foi utilizada inicialmente para pré-selecionar as 1.000 imagens mais semelhantes a uma imagem de referência escolhida por um clínico. Essas 1.000 imagens foram processadas utilizando-se o algoritmo de medida de similaridade na grade computacional. RESULTADOS: A precisão encontrada na classificação por análise de textura foi de 0,54 para imagens sagitais de joelho e de 0,40 para imagens axiais de cabeça. A análise de textura foi útil como filtragem, pré-selecionando imagens a serem avaliadas pelo algoritmo de medida de similaridade. A recuperação de imagens baseada em conteúdo utilizando o algoritmo de medida de similaridade aplicado nas imagens pré-selecionadas por análise de textura resultou em precisão de 0,95 para as imagens sagitais de joelho e de 0,92 para as imagens axiais de cabeça. O alto custo computacional do algoritmo de medida de similaridade foi amortizado pela grade computacional. CONCLUSÃO: A utilização da abordagem mista das técnicas de análise de textura e algoritmo de medida de similaridade no processo de recuperação de imagens baseada em conteúdo resultou em eficiência acima de 90 por cento. A grade computacional é indispensável para utilização do algoritmo de medida de similaridade na recuperação de imagens baseada em conteúdo, que de outra forma seria limitado a supercomputadores.

OBJECTIVE: To utilize the grid computing technology to enable the utilization of a similarity measurement algorithm for content-based medical image retrieval. MATERIALS AND METHODS: The content-based images retrieval technique is comprised of two sequential steps: texture analysis and similarity measurement algorithm. These steps have been adopted for head and knee images for evaluation of accuracy in the retrieval of images of a single plane and acquisition sequence in a databank with 2,400 medical images. Initially, texture analysis was utilized as a preselection resource to obtain a set of the 1,000 most similar images as compared with a reference image selected by a clinician. Then, these 1,000 images were processed utilizing a similarity measurement algorithm on a computational grid. RESULTS: The texture analysis has demonstrated low accuracy for sagittal knee images (0.54) and axial head images (0.40). Nevertheless, this technique has shown effectiveness as a filter, pre-selecting images to be evaluated by the similarity measurement algorithm. Content-based images retrieval with similarity measurement algorithm applied on these pre-selected images has demonstrated satisfactory accuracy - 0.95 for sagittal knee images, and 0.92 for axial head images. The high computational cost of the similarity measurement algorithm was balanced by the utilization of grid computing. CONCLUSION: The approach combining texture analysis and similarity measurement algorithm for content-based images retrieval resulted in an accuracy of > 90 percent. Grid computing has shown to be essential for the utilization of similarity measurement algorithm in the content-based images retrieval that otherwise would be limited to supercomputers.

Development of Histopathological Breast Tumor Image Retrieval System Based on Internet Using a Content-based Retrieval Method / 대한의료정보학회지

OBJECTIVE: We have developed breast tumor image retrieval system using content-based retrieval method. It compares the breast tumor image with Fibrocystic Change images, Ductal Carcinoma in Situ images and Invasive Ductal Carcinoma images and find most similar one. Since the final diagnosis for breast tumor image is done only by pathologist manually, this system can provide the objectivity and the reproducibility for determining and diagnosing the breast tumor. METHODS: The breast tumor image features used in the content-based image retrieval are color feature, texture feature and texture features of wavelet transformed images. And the system can be accessed through the internet. We used Windows 2003 as an operating system, Internet Information Server 6.0 as Web a server and ms-sql server 2000 as a database server. Also we use ActiveX Data Object to connect database easily. RESULT: We evaluated the recall and precision performance of the system according to the combinations of feature types and usage of partial or whole image. Results showed that the use of multiple features and whole image gave consistently higher rates compared to the use of single feature and partial image. CONCLUSION: This retrieval system can help pathologist determine the type of breast tumor more efficiently. Also it is working based on the internet, we can use it for researching and teaching in pathology later.

A novel classification-coding scheme for medical image retrieval / 医疗卫生装备

A novel mono-hierarchical muti-axiel classification coding scheme for medical image retrieval is proposed.The so-called MOAB coding scheme consists of four axes with three to four positions ranging from0to9,from A to Z.In particular,the modality code M describes imaging modality and relevant technical detail,and the orientation code O models examined body orientation.The anatomy code A refers to the body region examined and the biology code B describes the biological system examined.The MOAB classification-coding scheme enables a unique classifi-cation of medical images so that medical image retrieval can be efficient.The code is flexible and easy to be ex-tended.

Content-based Medical Image Retrieval / 医疗卫生装备

Medical Image has been increasingly applied in clinical diagnosis and treatment.It is very important to make use of large numbers of images in medical image management system in order to help clinician to analyze and diagnose.The traditional information retrieval techniques are not fit for retrieving large scale medical image databases.It is a very promising idea to introduce Content-based Image Retrieval(CBIR) technique into indexing medical image databases.The structure of the Content-based Medical Image Retrieval System(CBMIR) is introduced,and the key problems are mainly investigated,which included image segmentation,feature extraction,similarity searching and feedback mechanism.At last,the status and development of CBMIR are discussed.

Application of multi-band wavelets to medical image retrieval / 医疗卫生装备

Along with the increasing medical image data,it is imperative to set up an effective system about medical image retrieval.Essentially,the image classification is crucial for medical image retrieval.As a distribution pattern of image gray scale,texture is an important character.Wavelet multi-scale decomposition is essentially multi-channel filtering and its multi-resolution analysis structure is identical with human visual system.So the extraction of texture feature under different resolutions after multi-band wavelets transform is of great benefit to image recognition and image retrieval.Consequently,this paper designs an image classification method based on eight-band wavelet.This method solves the key technology in the medical image retrieval,and it gains very high classification rate.