MIRACLE at ImageCLEFmed 2007: Merging Textual and Visual Strategies to Improve Medical Image Retrieval

Villena-Román, Julio; Lana-Serrano, Sara; González-Cristóbal, José Carlos

doi:10.1007/978-3-540-85760-0_74

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…With a few rare exceptions, bacteria are single cells that divide into two cells, or binary fission, to increase their population. There are very small, protein-based viruses that lack genetic material called companion proteins [9]. In the biological sciences, an experiment may produce tens of thousands of photos.…”

Section: Types Of Leaf Spot Diseasesmentioning

confidence: 99%

Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network

Vignesh¹,

E²

2023

ESP-JETA

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Finding plant leaves is a crucial step in preventing a major outbreak. The automatic diagnosis of plant disease is an important research area. Similar to humans and other animals, plants also experience the negative effects of sickness. These diseases affect the entire plant, including the leaf, stem, fruit, root, and flower. More often than not, when a plant's sickness is left untreated, the plant bites the ground or can also cause the loss of leaves, blooms, natural products, and so forth. For accurate identification and treatment of plant diseases, these disorders must be properly dedicated. The study of plant infections, their causes, and methods for containing and managing them is known as plant pathology. However, the modern strategy emphasises human inclusion for order and differentiating disease evidence. This strategy is time-consuming and expensive. Programmable disease detection from plant leaf images using a sensitive registration technique may be more valuable than the existing one. In this research, we present a method for identifying symptoms and characterising plant leaf illnesses organically called Bacterial looking development based entirely Radial Basis Function Neural Network (BRBFNN). We employ bacterial looking streamlining (BFO), which also increases the speed and accuracy of the device, to give Radial Basis Function Neural Network (RBFNN) the best possible weight when understanding various illnesses at the plant Leaf's. The suggested method improves recognition of evidence and infection characterisation.

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Section: Types Of Leaf Spot Diseasesmentioning

confidence: 99%

Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network

Vignesh¹,

E²

2023

ESP-JETA

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“…Several examples of studies including non-image data have already been described [122,123]. Textual information has also been used to complement several studies that were part of the ImageCLEF medical challenge or used the same data [141][142][143][144][145][146][147].…”

Section: Retrieval Enhancement Using Non-image Datamentioning

confidence: 99%

A graph-based approach for the retrieval of multi-modality medical images

Kumar

Kim

Wen

et al. 2014

Medical Image Analysis

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Advances in sensors and imaging technologies are contributing to rapidly expanding data repositories that contain interrelated information from different modalities. The extraction and visualisation of knowledge from these repositories is a major challenge in the modern, digital world. In the medical domain, images are routinely acquired for a variety of tasks, including diagnosis and patient monitoring. Advances in imaging technologies have resulted in devices capable of acquiring images in multiple dimensions (volumetric and dynamic) as well as from multiple modalities. One example of a widely used volumetric and multimodality image is combined positron emission tomography and computed tomography (PET-CT), which presents physicians with complementary functional and anatomical features and spatial relationships. In clinical practice, PET-CT imaging has already proven its ability to improve cancer diagnosis, localisation, and staging compared to its single-modality counterparts.The clinical benefits provided by medical imaging have spurred increases in the data volume acquired in clinical environments. As such, massive medical imaging collections offer the opportunity for search-based applications in evidence-based diagnosis, physician training, and biomedical research. However, conventional search techniques that operate upon manually assigned textual annotations are not feasible for the volume of data acquired in modern hospitals. Qualitative text descriptions are also limited in their capacity to quantitatively describe the rich information inherent in medical images. Content-based image retrieval (CBIR) is an image search technique that utilises visual features as search criteria. CBIR has already demonstrated benefits forevidence-based diagnosis, physician training, and biomedical research by allowing clinical staff to consider relevant knowledge from retrieved cases. The majority of medical CBIR research has focused on single modality medical images leaving a clear deficiency in the retrieval of multi-modality images. In particular, images iii like PET-CT offer the ability for retrieval based upon the relationships between regions in different modalities, such as the location of tumour features (from PET) in relation to organ features (from CT). The challenge of multi-modality image retrieval for cancer patients lies in representing these complementary geometric and topologic attributes between tumours and organs. A secondary challenge lies in the human aspect of retrieval -effectively communicating the retrieved results to users and facilitating a better understanding of the similarity between the query and retrieved multi-modality images.As such, in this thesis we propose a new graph representation for multimodality images. Our representation preserves the spatial relationships between modalities by emphasising the inherent characteristics of these images that are used for disease staging and classification. This is done by structurally constraining the graph based on image features, e.g., spatia...

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“…These algorithms have been evaluated in text information retrieval showing an improved operation [21]. Other simple algorithms based on sets operations to merge ranking lists have been evaluated for image retrieval, using a text search engine and a content-based image retrieval system [19]. In addition, Lau et al [9] showed that linear combinations of text and visual rankings may lead to better results than each individual system.…”

Section: The 3rd Bcs Irsg Symposium On Future Directions In Informatimentioning

confidence: 99%

Multimodal Information Spaces for Content-based Image Retrieval

Caicedo

2009

Electronic Workshops in Computing

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Currently, image retrieval by content is a research problem of great interest in academia and the industry, due to the large collections of images available in different contexts. One of the main challenges to develop effective image retrieval systems is the automatic identification of semantic image contents. This research proposal aims to design a model for image retrieval able to take advantage of different data sources, i.e. using multimodal information, to improve the response of an image retrieval system. In particular two data modalities associated to contents and context of images are considered in this proposal: visual features and unstructured text annotations. The proposed framework is based on kernel methods that provide two main important advantages over the traditional multimodal approaches: first, the structure of each modality is preserved in a high dimensional feature space, and second, they provide natural ways to fuse feature spaces in a unique information space. This document presents the research agenda to build a Multimodal Information Space for searching images by content.

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MIRACLE at ImageCLEFmed 2007: Merging Textual and Visual Strategies to Improve Medical Image Retrieval

Cited by 7 publications

References 2 publications

Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network

Segmentation and Classification of Leaf Disease Using Radial Basis Function neural Network

A graph-based approach for the retrieval of multi-modality medical images

Multimodal Information Spaces for Content-based Image Retrieval

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