This paper is a review of the literature on detection of lung cancer using medical content based image retrieval. Lung cancer is prominent cancer as it states large number of deaths of more than a million every year. It creates need of detecting the lung nodule at early stage in Computer Tomography medical images. So to detect the occurrence of cancer nodule at early stage, the requirement of methods and techniques is increasing. There are different methods and techniques existing but none of them provide a better accuracy of detection. This provides content based image retrieval Computer Aided Diagnosis System (CAD) for early detection of lung nodules from the Chest Computer Tomography (CT) images. There are various phases described in the proposed CAD system. These are extraction of lung region from chest computer tomography (CT) images, segmentation of the lung region, feature extraction from the segmented region, and the classification of occurrence and non-occurrence of cancer in the lung. This paper describes the available literature and the techniques used for the detection of lung cancer.

Detection of Lung Cancer Using Content Based Medical Image Retrieval

---Medical Image Analysis is essential in order to detect and diagnose the various types of Cancers. In recent years there is a rise in the death rate of patients suffering from brain cancer and lung cancer. The chances of survival among people can increase if the detection is done in the earlier stage. The widely used diagnose technique is Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Images which are used to present the cancer location in the brain and lungs. In this work the brain tumour and lung cancer is detected and registered through the medical images in three stages. First is the pre -processing stage, a set of medical images is filtered for removing noise by Gaussian filter, Secondly the image is segmented automatically using Otsu and KNN clustering. Edge Detection method is done by using canny detection method. Third stage is the feature extraction stage, in this stage the segmented MRI and CT images are registered to obtain the tumour. The feature detection methods used are the SIFT and SURF algorithm for both brain and lung images to obtain effective results. The SIFT and Affine Transform registration technique used increases the speed and reduces the complexity of geometrical alignments of two images that is the reference and sensed images. It also displays the minute difference between two identical images rapidly and accurately which is essential for medical diagnoses.

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Medical Image Analysis of Image Segmentation and Registration Techniques

This paper discusses a parallel immune algorithm (IA) for detection of lung cancer in chest X-ray images based on object shared space. The template matching method is combined to the algorithm and Java Spaces is used as object shared space. The experiment results show that the algorithm is efficient for detecting suspected lung cancer in chest X-ray images.

Parallel Immune Algorithm for Lung Cancer Detection in X-Ray Images Based on Object Shared Space

Recently, the notion of Dataspace has been introduced as a virtual space where many data sources are managed regardless of their structures and locations; this, with the aim to return best efforts answers to user's queries. In fact, a dataspace is a set of participants which are data sources connected to the dataspace and a set of relationships between those participants. A data source, a participant, could be a file, a relational database, XML repository, web pages and so on. However, no formal definitions about relationships which could be built among the data sources have been proposed. Therefore, one of the challenges occurring in this new abstraction of data management is to incrementally build meaningful relationships between sources. In this paper, we introduce SEM-HDM, a SEMantics-based Heterogeneous Data Management which aims at constructing semantic relationships between heterogeneous sources of data based on a defined syntax and semantics. In fact, SEM-HDM first constructs a Semantic dictionary and then a reasoning dictionary which is improved incrementally by analyzing user's activities on a given dataspace. We show on an example of three heterogeneous sources of data how SEM-HDM builds his semantic and reasoning dictionary.

/pdf/building-semantic-relationships-incrementally-in-dataspace-1ee0aajfzz.pdf

Building Semantic Relationships Incrementally in Dataspace

A comprehensive review of content-based image retrieval systems using deep learning and hand-crafted features in medical imaging: Research challenges and future directions

This paper presents a case study of an image retrieval system based on a notion of similarity between images in a multimedia database and where a user request can be an image file or a keyword. The CBIR (content based image retrieval) system, the current system of search for information (SSI) -e.g. PEIR, MIRC, MIR, IRMA, and Pathopic- and the current search engines (CSE) -e.g. Google, Yahoo and Alta Vista- make image search possible only when the query is a keyword. This type of search is limited because keywords are not expressive enough to describe all important characteristics of an image. For example, an exact match request cannot be formulated in such systems and in SSI system, users should know natural language (e.g. English, French or German) used. We used XIRS (an XML image retrieval system) to set up a similarity distance between images, then to compare the request image with those in a database. An experimentation of XIRS on lung cancer diagnosis is presented. The statistics show that our system is more efficient than leading CBIR systems such as ERIC7, PEIR, PathoPic and CSE.

/pdf/a-case-study-of-image-retrieval-on-lung-cancer-chest-x-ray-3ca0h3ph3n.pdf

A case study of image retrieval on lung cancer chest X-ray pictures

XQuery and Keyword query are the main methods to get XML data. But the former is too complicated for non-professional users and the latter doesn't have enough semantic capabilities to capture user's intents completely. Based on data models, this paper analyzes the reasons which lead to the drawbacks of existing query methods. And then, a novel auto-navigating XML query model is proposed. The query model is composed of the XML data model, the query language and the query algorithm. The data model stores XML documents in a clustering way according to the semantic relativity. The query language has the intuitive grammars and is suitable for non-professional users to use. The query algorithm has auto-navigating capability and can find the target data from XML documents without complete structure information.

http://www.wseas.us/e-library/conferences/2007beijing/papers/554-220.pdf

Lin Xudong

Papers

A case study of image retrieval on lung cancer chest X-ray pictures

NNQM: a novel non-navigating XML query model