Color doppler echocardiographic image analysis via shape and texture features

Nandagopalan, S.; Dhanalakshmi, C.; Adiga, B. S.; Deepak, N.

doi:10.1109/icbbt.2010.5478992

Cited by 5 publications

(7 citation statements)

References 7 publications

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“…From this video, correct frames are selected corresponding to end-diastole and end-systole to compute measures such as ejection fraction. 7 Since every image has both text area and display area, we crop the display area and resize it to 4006250 so that segmentation can be speeded up. We cannot use these images directly for analysis, because ultrasound images suffer from an inherent imaging artefact called speckle.…”

Section: Echo Image Acquisition and Pre-processingmentioning

confidence: 99%

“…All 2D echo images are basically greyscale images with 24 bits per pixel and this means that for each pixel, the RGB values are the same. However, colour Doppler images are pseudo colour images 7 where the RGB values of colour pixels are not the same.…”

Section: Clustering the Imagesmentioning

confidence: 99%

“…7 This means that only the colour portion of the image is considered for feature extraction. From this colour segment, qualitative features like colour histogram, kurtosis, skewness, texture, etc., are computed 7 and stored in the feature vector.…”

Section: Introductionmentioning

confidence: 99%

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A content-based image retrieval system for echo images using SQL-based clustering approach

Nandagopalan

Adiga

Sudarshan

et al. 2012

The Imaging Science Journal

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Content-based image retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images. CBIR from medical image databases does not aim to replace the physician by predicting the disease of a particular case but to assist him/her in diagnosis. The visual characteristics of a disease carry diagnostic information and oftentimes visually similar images correspond to the same disease category. By consulting the output of a CBIR system, the physician can gain more confidence in his/her decision or even consider other possibilities. In this paper, we aim at building an efficient content-based echo image retrieval (CBEIR) system. Echocardiography provides important morphological and functional details of the heart which can be used for the diagnosis of various cardiac diseases. Normally two-dimensional (2D) echo and colour Doppler image modalities are used for analysis and clinical decisions. From 2D echo images, features such as dimensions of cardiac chambers (area, volume, ejection fraction, etc.) are extracted, whereas texture properties, kurtosis, skewness, edge gradient, colour histogram, etc., are extracted from colour Doppler images. Hence, this forms a multi-feature descriptor which then is used to retrieve similar images from the database. A novel clustering approach merged with the traditional CBIR model is used for development in order to speed up the retrieval and enhance the accuracy of retrieval. The main focus of our work is the following: efficient segmentation algorithm, accurate detection of cardiac chambers, new and fast method to obtain colour portion of the Doppler image, and finally is able to categorise the type of disease and the severity level. These domain-specific low-level features are very important to build a reliable and scalable CBIR model. The similarity values are obtained by Euclidean distance metric. The feature database is basically a set of quantitative and qualitative features of the images. Our image database is populated with diverse set of approximately 623 images extracted from 60 normal and abnormal patients acquired from a local cardiology Hospital. Exhaustive experimentation has been conducted with various input query images and combinations of features to compute the retrieval efficiency which are validated by domain experts. It has been shown through recall-precision graphs that the proposed method outperforms compared to others reported in the past.

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Section: Echo Image Acquisition and Pre-processingmentioning

confidence: 99%

Section: Clustering the Imagesmentioning

confidence: 99%

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A content-based image retrieval system for echo images using SQL-based clustering approach

Nandagopalan

Adiga

Sudarshan

et al. 2012

The Imaging Science Journal

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“…It is experimentally verified that a maximum of 6-8 iterations is sufficient to get good segmentation results. Euclidean distance, d is calculated with a single SQL statement for all pixels w. r. t the centroids without join (Nandagopalan et al, 2010b).…”

Section: Delete Table Datamentioning

confidence: 99%

“…This algorithm can be used both for 2D echo and color Doppler images. To achieve further enhancement in speed, for all table updates a novel idea of TRUNCATE-INSERT combination is used (Nandagopalan et al, 2010b).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Medical Image Segmentation

Shanmugam¹

2011

Journal of Computer Science

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Problem statement: Segmentation is a vital aspect of medical imaging. It aids in the visualization of medical data and diagnostics of various diseases. Ultrasound image segmentation, in particular echocardiographic image segmentation, is required to identify the regions of interest such as Left Ventricle (LV) and other cardiac cavities. Existing methods do not address the drawbacks of speed and quality of segmentation. A faster method is required for effective, accurate and scalable clinical analysis and diagnosis. Approach: In this research, a novel approach is used to segment the 2D echo images of various views. A modified K-Means clustering algorithm, called "Fast SQL K-Means" is proposed using the power of SQL in DBMS environment. In K-Means, Euclidean distance computation is the most time consuming process. However, here it computed with a single database table and no joins. This method takes less than 10 sec to cluster an image size of 400×250 (100K pixels), whereas the running time of direct K-Means is around 900 sec. Since the entire processing is done with database, additional overhead of import and export of data is not required. The 2D echo images are acquired from the local Cardiology Hospital for conducting the experiments. Results: The proposed algorithm was tested by considering a number of echo images in apical four chamber, long-axis and short axis views. We have compared the direct K-Means implementation with the proposed algorithm by varying the data size from 10-100K and found that the results outperformed compared to the results of other authors. The pattern of the data and the number of clusters had almost no impact on the clustering time. Conclusion: An efficient and nontraditional model for echo image segmentation is presented by using the SQL. Fast algorithms are required for immediate analysis of echo images within ICUs, remote places, telemedicine. The challenge is that ultrasound images are prone to speckle noise, segmented echo images carry gaps in the cardiac regions which in turn causes difficulties in boundary tracing and selection of seed values for the K-Means. Future research can enhance the speed by partitioning the database tables and use of parallel SQL statements.

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