Medical Image Compression with Lossless Region of Interest Using ...

International Conference on Computational Techniques and Mobile Computing (ICCTMC'2012) December 14-15, 2012 Singapore

Medical Image Compression with Lossless Region of Interest Using Fuzzy Adaptive Active Contour Loganathan R.1, Dr. Y. S. Kumaraswamy2

medical and scientific imaging, lossless compression is usually used due to the fear of losing critical information. It is said that loss of minor details leading to subsequent medical diagnosis/scientific post-processing can result in legal liabilities/incorrect diagnoses. But, the medical image compression community argues that lossy medical image compression is necessary and helpful in the long run [4]. They contend that technology must be considered as the major chunk of medical data requires remote storage of hard-copy films, frequently resulting in loss or damage and always needing time to locate and transfer. Thus, instead of thinking in terms of lossless-lossy dichotomy, alternative ways must be sought to compromise conflicting requirements. In such a scenario more users in medical and scientific communities have started to accept “near-lossless” methods as a tradeoff between compression ratios and distortion to enable achievement of higher compression ratios with limited distortion to ensure accuracy for specific purposes. Medical imaging impacts medicine specially diagnosis and surgical planning. But imaging devices generate a large amount of data for each patient requiring storage and efficient transmission. Present compression schemes have high compression rates when quality loss can be afforded. But physicians cannot afford deficiencies in image regions which are important, known as regions of interest (ROIs). An approach which brings high compression rates accompanied by good quality in ROI’s is needed. A common idea is to preserve quality in diagnostically critical regions while allowing lossy encoding of other regions. The research‘s aim focuses on ROI coding to ensure use of multiple and arbitrarily shaped ROIs in images, with arbitrary weights describing the importance for each ROI including background (i.e. image regions not of ROI) so that latter regions can be represented by varying levels of quality. From the introduction of snakes [5], active contours were applied to various problems, like image segmentation, feature extraction and image registration. These traditional snakes and contour models are called “edge-based” models, as they are based on edge functionals to prevent curve evolution locating objects with gradient defined edges. Hence, performance of pure edge-based models is usually not enough. There was a lot of research in designing complex region-based energy functionals that were unlikely to yield undesirable local minima in comparison with edge-based energy functionals.

Abstract—Image segmentation is a major task in image analysis and computer vision. Though many methods were suggested in literature, designing robust and efficient segmentation algorithms is a big challenge still because of the variety and image complexity. From the introduction of snakes, active contours were applied to various problems, such as image segmentation of Region of Interest (ROI), feature extraction, image registration, etc. In this paper, a Fuzzy based Adaptive Active Contour is proposed for segmentation of Region of Interest to achieve higher compression rate. A novel biorthogonal wavelet is proposed for compression technique.The ROI and Non-ROI segments obtained by Fuzzy Adaptive Active Contour are compressed using lossless and lossy compression respectively.

Keywords— Medical images, MRI, image compression; region of interest; active contour, Fuzzy Adaptive Active Contour. I. INTRODUCTION

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S medical imaging facilities are usually digitalized, image compression for storage and transmission is important. Image compression is a must for all telematic applications and has a major part in ensuring good quality of service [1]. Medical image compression is necessary to ensure fast and reliable transmission through lower bandwidth for diagnosis in remote locations. The challenge for digital medical image compression is that even though high image compression rate is wanted, usability of reconstructed images is dependent on important characteristics of the original image that requires preservation for correct diagnosis [2]. Image segmentation is a major task in image analysis and computer vision. Though many methods were suggested in literature, designing robust and efficient segmentation algorithms is a big challenge still because of the variety and image complexity [3]. IMAGE compression techniques are categorized as lossy and lossless. The former achieves high compression ratios through various strategies effectively retaining visually relevant information. The latter gets modest compression ratios due to cons4rvative maintenance of information. In Loganathan R. Research Scholar, Department of Computer Science and Engineering, Sathyabama University, Chennai, Tamilnadu, India (corresponding author phone: +919448417664; fax:+91442450 2344; email:[email protected]). Dr. Y. S. Kumaraswamy. Senior Professor and Head, Department of MCA(VTU), Dayananda Sagar College of Engineering, Bangalore, Karnataka, India (e-mail: [email protected]).

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This paper looks at this issue through presentation of a novel fuzzy energy-based active contour that handles objects with boundaries not defined by gradient, ie; objects with smooth/discontinuous boundaries. Fuzzy logic is a heavily used tool in data clustering, but not in active contouring. Usually, fuzzy methods give accurate and more robust data clustering, and hence it is combined with active contour methodology, to introduce a new model called fuzzy energybased minimization. The energy fuzziness leads to a balanced technique with the capability of rejecting “weak” local minima. In this paper, it is proposed to segment the medical images using proposed ROI based segmentation technique, Fuzzy Adaptive Active Contour method. Further to enhance image compression rate, a modified biorthogonal wavelet is proposed. The rest of the paper is organized as follows: section 2 deals with related works, section 3 explains the techniques used in this paper, section 4 tabulate the results and section 5 concludes the paper.

yielded from the proposed method reveal that joining boundary and region information provides exact texture segmentation and more robustness. In image segmentation, the edge-based and region-based active contours are commonly implemented. The small neighborhoods of pixels are characterized by the edges and the whole image regions which might possess overlapping probability densities are characterized by the region descriptors. Cristina Darolti et al., [8] defined Local Region Descriptors (LRDs) in order to characterize image regions locally. The important feature statistics of these includes from the pixels positioned inside the windows centered lying on the evolving contour, and this is capable to decrease the overlap among distributions. On level sets, to define general-form energies LRDs are employed. Mostly, through the logarithm of the probability density of features conditioned on the region, a specific energy is related with an active contour. Two new functions are introduced to decrease the local minima number of these energies. These two functions construct the energy functional that are based on the supposition that local densities are almost Gaussian. Including confidence intervals, the first one implement a similarity measure among features of pixels. A local Markov Random Field (MRF) model is implemented by the second one. Active contours that can segment objects that have largely overlapping global probability densities are achieved by reducing the associated energies. Therefore, in very limited time while employing a fast level-set implementation, natural large images are segmented precisely using the proposed method that is proved from our experimentation. Darsana et al.,[9] proposed an approach that combines automatic relevance feedback and a modified stochastic algorithm to prevent the semantic gap in image retrieval. Using combined feature vector, from the image database a visual feature database is constructed. In this step, only a few fast-computable features are considered. The system ranks the entire dataset based on the query image selected by the user. The first automatic relevance feedback is generated by retrieving the nearest images. Using Latent Semantic Indexing, the combined similarity of textual and visual feature space is evaluated, and labeling of the images as relevant or irrelevant is done. A feature re-weighting process is derived by feedback and to the particle swarm optimizer it is routed. Unlike the conventional swarm update approach, in this approach every swarm is divided to execute search parallelly, and hence the performance of the system is improved. The proposed approach gives an effective space exploration mechanism and also powerful optimization tool. The goal of the proposed approach is to cluster relevant images implementing meta-heuristics and to dynamically modify the feature space by feeding automatic relevance feedback without any human interference. SteliosKrinidis et al., [10] introduced a new fast framework for active contours based on techniques of curve evolution in order to detect objects in an image. The proposed model facilitates the detection of objects based on the minimization of a fuzzy energy, for the objects whose boundaries are not

II. RELATED WORKS For active contours, an effective external force is Gradient vector flow (GVF) which has an isotropic nature that handicaps its performance. As only the diffusion is kept along the normal direction of the isophotes, the NGVF model proposed currently is anisotropic but is sensitive to noise and able to remove weak boundaries. Yuanquan Wang et al., [6] proposed the normally biased GVF (NBGVF) external force for snake models. This external force keeps the diffusion along the tangential direction of the isophotes and biases along the normal direction. In the boundaries, the biasing weight approaches zero, and in homogeneous regions it is one. As a result, the proposed NBGVF snake is able to preserve weak edges and smooth out noise when the other desirable properties of GVF and NGVF snakes like insensitivity to initialization, enlarged capture range and convergence to ushape concavity are maintained. Evaluation of these properties is performed in real and synthetic images. The results obtained reveal that NBGVF snake outperforms NGVF and GVF snakes. Therefore, NBGVF snake is an excellent substitute to the NGVF and GVF snakes. Chen Sagiv et al., [7] discussed in the context of the Gabor feature space of images, the problem of textured image segmentation. To form the Gabor feature space, the Gabor filters are modified to a series of orientations, frequencies and scales are employed to the images. Using the Beltrami framework, the local features using two-dimensional Riemannian manifold is wheedled out. A perfect indicator of texture changes is provided by the metric of this surface and is also implemented for texture segmentation in a geodesic active contours algorithm and in a Beltrami-based diffusion mechanism. To compare the performance of the proposed algorithm applied for texture segmentation is done with the edgeless active contours algorithm. Extending the geodesic and edgeless active contours methods to texture segmentation that is an integrated method is also proposed. The results 188


essentially defined by gradient that can be observed as a specific case of a minimal partition problem. For the model motivation power evolving the active contour, this fuzzy energy is applied that stops on the desired object boundary. Unlike the most classical active contours, the stopping term does not depend on the gradient of the image, but it is associated to the image color and spatial segments. A balanced technique with a robust ability to reject “weak” local minima is provided by the fuzziness of the energy. Quickly, the proposed approach converges to the desired object boundary as it cannot solve the Euler-Lagrange equations of the underlying problem, yet it calculates directly the fuzzy energy alterations. The proposed fuzzy energy-based active contour presents the theoretical properties and different experiments that illustrate the high-throughput and more robustness of the approach based on the gradient or other type of energies over other classical snake methods. In order to enhance the performance of interactive Content Based Image Retrieval, Krishna Chandramouli et al., [11] presented effective exploitation of user logs. By analyzing the user logs produced from the relevance feed-back information obtained by multiple users for a collection of target queries, a fuzzy membership function is derived. On the basis of Self Organising Maps, the underlying machine learning algorithm for User Relevance Feedback is present. By Particle Swarm Optimisation, the training of network nodes is accomplished. Using two datasets namely Corel 700 and Flickr 500, the proposed scheme is estimated and validated.

features refer to the edge strength, yielding edge-based active contours, or to the region’s characteristics occupied by processed image objects, yielding region-based active contours. The segmentation process begins with an initial contour received automatically or through user interaction and is evolved toward object boundaries under forces derived from the energy, energy minimization being accomplished by gradient descent. The first active-contour methods were edge-based, using functionals which depended on image response to an edge filter [5, 12, 13] such that the size of motion forces derived from functionals is small when edge strength is large. Segmentation with an edge-based active contour can be affected by edge detector problems. Weak/undetected perceptual edges cause active contour to pass across real boundaries, while undesired strong edges stop the contour. This results in a drawback of edge-based active contours with a small range of capture, requiring the initial contour to be in proximity to objects needing segmentation. Intensity information from image regions delimited by a contour is added to an edge-based energy functional to make active contours still more robust [14]; still more results for energies relying on a linear combination of edge and region terms can be found [15]. The basic Active model is given by 𝐹(𝑐 + , 𝑐 − , 𝐶) = 𝜇. 𝐿𝑒𝑛𝑔𝑡ℎ(𝐶) + 𝜆+ ∫𝑖𝑛𝑠𝑖𝑑𝑒(𝐶) |𝒰0 (𝑥, 𝑦) − 𝑐 + |2 𝑑𝑥𝑑𝑦 (2) +𝜆− ∫∮ 0 and λ+,λ-> 0 are weights for the regularizing term and the fitting term, respectively. The above model can be rewritten as

A. Modified Biorthogonal Wavelet The wavelet analysis procedure is for adopting a wavelet prototype function, known as analyzing wavelet or mother wavelet. Temporal analysis is done through a contracted, highfrequency prototype wavelet version, while frequency analysis is executed with a dilated, low-frequency version of the same wavelet. As the original signal/function is represented in terms of wavelet expansion, data operations are performed using the corresponding wavelet coefficients alone. And if the best data adopted wavelets or truncate coefficients below the threshold is chosen, data representation becomes sparse, and this sparse coding makes wavelets excellent tools in data compression.

𝐹(𝑐 + , 𝑐 − , ∅) = 𝜇. 𝐿𝑒𝑛𝑔𝑡ℎ{𝜍 + 𝜆− ∫∅