We propose a new hybrid approach to content-based image retrieval.
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Contrary to the single feature vector approach which tries to retrieve similar images in one step, this method uses a two-step approach to retrieval. In the first step, we propose the use of a neural network called Self Organizing Map SOM for clustering the images with respect to their basic characteristics.
In the second step, the GA based search will be made on a sub set of images which were having some basic characteristics of the input query image. We applied our approach to a database of high resolution mammogram images and show that this method radically improves the retrieval precision over the single feature vector approach. To determine whether our CBIR system is helpful to physicians, we conducted an evaluation trial with five radiologists. Moreover, this method is faster and has higher retrieval accuracy compared to the single stage methods.
Breast cancer remains to be a leading cause of death among women in the developed countries. Currently mammography is the dominant method for detection of breast cancer. Mammography is an x-ray examination of the breast. It is used to detect and diagnose breast disease in women who either have breast problems such as a lump, pain, or nipple discharge, as well as for women who have no breast complaints. The procedure allows detection of breast cancer s, benign tumors and cysts before they can be detected by palpation touch.
In spite of the technological advances in recent years, mammogram reading still remains a difficult clinical task. Some breast cancer s may produce changes in mammograms that are subtle and difficult to recognize Strickland and Hahn, Furthermore, it is very difficult to distinguish benign lesions from malignant ones in mammograms.
As a result, between 2 and 10 women are biopsied for every cancer detected, causing needless fear and pain to women who are biopsied Sickles, Due to the subtlety in the appearance of individual Micro Calcifications MC , there is a significant risk that a radiologist may misclassify some cases in breast cancer diagnosis Wei et al. Training a neural network model essentially means selecting one model from the set of allowed models that minimizes the cost criterion.
There are numerous algorithms available for training neural network models. Most of them can be viewed as a straightforward application of optimization theory and statistical estimation. Most of the algorithms used in training artificial neural network s are employing some form of gradient descent. This is done by simply taking the derivative of the cost function with respect to the network parameters and then changing those parameters in a gradient-related direction.
It is trained using unsupervised learning to produce low dimensional representation of the training samples while preserving the topological properties of the input space. This makes SOM reasonable for visualizing low-dimensional views of high-dimensional data, akin to multidimensional scaling.
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The model was first described by the Finnish professor Teuvo Kohonen and is thus sometimes referred to as a Kohonen map. In this study, we use SOM neural network to cluster mammogram images into three distinct groups based on the characteristics of the background tissue of the mammograms.
Kohonen described the structure of SOM network. Genetic algorithms are search algorithms based on the mechanics of natural selection and natural genetics inspired by evolutionary biology such as inheritance, mutation, selection and crossover also called recombination. It is used in finding true or approximate solutions to optimization and search problems.
GA is categorized as global search heuristics. Genetic algorithms are implemented as a computer simulation in which a population of abstract representations called chromosomes or the genotype or the genome of candidate solutions called individuals, creatures, or phenotypes to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary, as strings of 0 and 1 sec, but other encoding are also possible.
The evolution usually starts from a population of randomly generated individuals and happens in generations.
In each generation, the fitness of every individual in the population is evaluated, multiple individuals are stochastically selected from the current population based on their fitness and modified recombined and possibly mutated to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population. Images have always been used in medicine for teaching, diagnosis and management purposes.
Now medical imaging systems produce more and more digitized images in all medical fields: visible, ultrasound, X-ray tomography, MRI, nuclear imaging, etc. These images are very useful for diagnostic purposes. They are directly related to the patient pathology and medical history.
Volume 21 Issue 2 | Journal of Electronic Imaging
However, the amount of images we can access nowadays is so huge that database systems require efficient indexing to enable fast access to images in databases. Despite the progress made in the general area of image retrieval in recent years Bimbo, , its success in biomedical thus far has been quite limited Wong, Automatic image indexing using CBIR is one of the possible and promising solutions to effectively manage image databases Smeulders et al.
The visual characteristics of a disease carry diagnostic information and oftentimes visually similar images correspond to the same disease category.
The processing scheme adopted in the proposed system focuses on the solution of two problems. One is how to detect the ROI as suspicious regions with very weak background and another is how to extract features that characterize the suspicious regions. Many image collections contain few or no index terms. To search these collections, a set of techniques known as CBIR is used. The CBIR is a way to index or find a similarity between images in a database. The matching process between image search example and stored image content measures are complex and require sophisticated data management support.
Retrieval by image content has received great attention in the last decades. Several techniques have been proposed to the problem of finding or indexing images based on their contents EI-Naga et al. Each method used has strong and weak points. All these traditional approaches to CBIR represent each image in the database by a vector of feature values Flickner et al. A hierarchical learning framework for retrieval of relevant mammogram images has been reported in EI-Naqa et al. A wavelet based Image retrieval method was proposed by Lamard et al.
volunteerparks.org/wp-content/fiqisim/2183.php De Azevedo-Marques et al. But all these study have not mentioned about the speed of retrieval of masses. Persent earlier studies on functional magnetic resonance image retrieval Jose and Mythili, shows genetic algorithm will study fast for content-based image retrieval. In this study, we address both accuracy and speed of image retrieval. The results are more promising and accurate. The proposed retrieval system is in principle very different and may helpfully complement existing diagnostic aids.
If we view the human observer as a classifier, then the aim of the CBIR system is to provide the observer with training-set examples that are close to his decision boundary. We expect such a facility to be useful in medical education and training as well.
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In this study we have proposed a method to provide the radiologist with a set of images from past cases that are relevant to the one being evaluated, along with the known pathology of these past cases. A library of relevant past images would assist radiologists to diagnose difficult cases in a better ways.
The goal of the proposed CBIR is to obtain those mammograms that are similar in content to the query mammogram from a possibly very large mammogram database. Overview of the proposed image retrieval framework: The proposed framework is illustrated with a functional diagram in Fig. This framework will facilitate to search similar images in a large scale database with reasonable computational complexity.
Searching the entire database for similar images will increase the time required to retrieve similar images. Based on the character of background tissue mammogram images can be classified as F-Fatty, D-Dense-glandular and G-Fatty-glandular. Hence, the proposed model is divided in to two stages. In the second stage, the query image is obtained and the ROI is selected by the radiologist.
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As and when the ROI is selected the system acquire the length l and breadth b and then the class of the query image is identified using the same SOM network. Once, the class of query image is identified searches for similar suspicious region in the corresponding database with the same dimension of the ROI lxb will be followed. SOM algorithm Kohonen, is a neural network algorithm based on unsupervised learning. Basically it performs a vector quantization on the histogram of the images in the database and simultaneously organizes the quantized vectors on a regular low-dimensional grid.
The block diagram of SOM classification is as shown in Fig. Histogram of the image is chosen as input to the SOM since it is very simple to calculate. While finding, the histogram of a mammogram images, the continuous black background as well as the over exposed white regions will add considerable amount of error in the histogram output.