CHAPTER 5 CORONARY ANGIOGRAM VIDEO COMPRESSION USING WAVELET BASED CONTOURLET TRANSFORM AND REGION OF INTEREST TECHNIQUE

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1 123 CHAPTER 5 CORONARY ANGIOGRAM VIDEO COMPRESSION USING WAVELET BASED CONTOURLET TRANSFORM AND REGION OF INTEREST TECHNIQUE 5.1 INTRODUCTION Coronary angiograms play an important role in the diagnosis and treatment procedures of heart diseases. Each angiogram procedure results in sequence of images each of spatial size and a bit-depth of about 8 bits/pixel and is represented by few gigabytes of data, which demands considerable amount of memory and band-width for transmission. The storage and transmission problems can be removed significantly by compressing the angiogram image sequences. A lossless compression technique has been incorporated in the angiographic application profile of DICOM 3.0-standard, but it allows only a maximum compression of about 2:1 using lossless JPEG method (Zaid and Fradj 2010). Lossy compression techniques could significantly reduce the amount of data associated with the angiogram sequences under the stipulation that losses caused do not affect the diagnostic quality of the angiogram image. During compression procedures coronary angiogram images have to be handled carefully, since the loss of image quality in those images have a high impact on diagnostic accuracy. The factors responsible for this are the small size of coronary arteries and their poorly-defined edges (Zaid and Fradj

2 ). A compression technique specifically designed for this type of data considering all the factors mentioned above is required. In this chapter, a lossy compression method for coronary angiogram video sequences has been proposed. In the proposed method the significant regions of the angiogram sequence have been detected and represented as a binary Region of interest (ROI) mask and the mask is sub-sampled further to be equivalent to the size of subbands at different levels. The angiogram image sequence has been divided into frames and transformed using WBCT. The differences in parent-child relationship among the WBCT coefficients are managed by the repositioning algorithm. The WBCT coefficients corresponding to insignificant areas are efficiently removed and the coefficients are SPECK coded. The proposed method has been applied to realtime data sets and the results were studied. The results indicate that the proposed method outperforms JPEG 2000 both in CR and PSNR and it outperforms Wavelet based SPIHT in CR maintaining a similar PSNR. The evaluation results obtained from the Cardiologists indicate that the proposed method could reproduce the diagnostic features of the angiogram acceptably. 5.2 RELATED WORK Traditional video compression schemes using motion estimation and compensation techniques and DCT may not be optimal for compressing coronary angiogram video since the blocking artefacts caused due to block based motion estimation and blocked DCT may obstruct the diagnosis (Zaid and Fradj 2010). Beretta et al. (1994) proposed a method for the lossy compression of cardiac angiogram images. The method uses Full-Frame Discrete Cosine

3 125 Transform (FFDCT) along with an optimized bit allocation. Their method has been compared with standard JPEG. From the experimental results, the authors state that at a compression ratio of 12:1, the reconstructed image quality of their method is better than base line JPEG. The authors also claim that their method does not induce blocking artefacts and the fine details of the angiogram images are preserved. Ho et al. (1996) proposed a method for the compression of angiogram images which achieves a compression ratio of about 20:1 without introducing blocking artefacts. The method adopted motion compensated prediction to remove the inter frame correlations, the difference images arising from ME are encoded by discrete wavelet transform. Moreover, the authors have developed a classification scheme which labels each block in an image as intra, error or background and encodes the blocks consequently. The authors claimed that their DWT based approach when applied to coronary angiograms provide high image quality and high compression. Gibson et al. (2002 and 2004) suggested a wavelet based compression approach for the angiogram video. The authors state that their method is based on the philosophy that diagnostically significant areas of the image should be allocated more number of bits. Their approach uses a 3-D wavelet coder based on SPIHT coding. In the compression framework a region of interest detection stage and a texture-modelling stage has been included. The method has been applied to real-time data sets and evaluation has been done by using Root Mean Square Error (RMSE) and trained Cardiologists. The authors claim that their method could operate at around a CR of 20:1 without losing diagnostically significant information.

4 126 Zaid and Fradj (2010) developed a coronary angiogram video compression method based on the latest video coding standard, H.264/AVC. The authors have modified the coding system by integrating a classification process that labels each fixed size region in the image as relevant or irrelevant and encoding it accordingly. The method has been compared with baseline H.264/AVC and the authors claim that their method maintains the compression efficiency of H.264/AVC but in decreased computational cost. In their study, consultant Cardiologists have been asked to conduct a review on reconstructed images at different compression ratios. Based on the Cardiologist s decision, the authors state that their method can perform a compression of about 30:1 without disturbing the diagnostic quality of the image. 5.3 PROPOSED METHOD In the proposed method, the significant regions of the angiogram sequence are detected by using the difference between consecutive frames of the sequence. The significance information is represented in the form of a binary mask. To equal the size of subbands at different levels of decomposition, the mask is sub-sampled to different sizes. The angiogram image sequence to be compressed is divided into individual frames and the frames are transformed using WBCT. This is achieved by performing wavelet decomposition on the image frame followed by a directional decomposition on the wavelet coefficients using directional filter bank. The differences in the parent child relationship among the WBCT coefficients are managed by the repositioning algorithm proposed by Eslami and Radha (2004). During repositioning the children in two different directional subbands are arranged such that they are present in one band. The detailed explanation of the

5 127 repositioning algorithm is presented in Chapter 4. After repositioning, the lowest two levels of WBCT coefficients that correspond to insignificant areas identified by ROI binary mask are made zero. The resultant data are then encoded by SPECK algorithm. The simulation results show that the proposed coder provides significant gains over the existing compression schemes. The detailed explanation of wavelet based contourlet transform, biorthogonal wavelet CDF (9/7), directional filter bank and SPECK coding is presented in Chapter 1. The entire compression and decompression procedures are explained in Figures 5.1 and 5.2. The ROI detection technique of the proposed method is described below Region of Interest (ROI) Detection The purpose of the ROI detection technique is to segment an angiogram image into two distinct regions, one region containing the diagnostically significant areas of the angiogram, and the other containing the less important background areas slightly modified from the method proposed by Gibson et al. (2002, 2004). The significant regions of diagnostic concern in the angiogram sequence are the one that contain coronary arteries. The following procedure identifies the presence of arteries in all the frames of the sequence. A set of image frames, indexed as {0, 1 f-1}, extracted from an angiogram sequence (I) is considered. The difference frames are obtained by finding the variation between successive frames in the sequence and they are denoted as {D 1 D f-1 } D =I I (5.1)

6 128 The binary ROI mask R is generated from this set of difference image frames {D 1 D f-1 }. The binary mask identifies regions containing the important arteries in all the frames of the sequence. The procedure for estimating the binary ROI mask can be described by a four-step process Deviation calculation The variation of individual difference frames in set D from its mean value is calculated and averaged for all the difference frames through the equation defined by (Gibson et al. 2002, 2004) D (x,y)= 1 f 1 D (x,y) m (5.2) In Equation (5.2) f represents the total number of frames in the sequence, D i represents a single frame from D and m i represents the mean value of D i Gaussian filtering A two-dimensional Gaussian filter is applied to D s (x, y) to remove the background noise and to maintain the connectivity of arteries (Gibson et al. 2002, 2004) Thresholding A pixel (x,y) is defined as a part of ROI if R(x,y)= 1 D (x,y) T (5.3) 0 otherwise

7 129 In Equation (5.3) D g is the Gaussian filtered function of D s. The threshold (T) in the equation is determined by the method proposed by Otsu (1979) Representation of ROI in blocks The binary ROI mask R(x,y) is converted into a block based representation. A block of size has been used in the proposed method. If the block contains one pixel belonging to ROI then the entire block is considered as a part of ROI. Hence, the ROI is expanded firmly (Gibson et al. 2004). In the proposed method the insignificant coefficients corresponding to the two lowest level subbands are getting eliminated whose sizes are almost half and quarter of the size of the image frame hence a sub sampling is done to R(x,y) using Equation (5.4) from Gibson et al. (2004). (, )= ( +, )= (, + )= ( +, + )= (, )= (5.4) The mask R 0 is the sub-sampled version of R with half of its spatial size in each dimension. A pixel (x,y) in R 0 will be considered as a ROI pixel if any of the equivalent 4 pixels of R in Equation (5.4) are labelled as ROI pixel. The mask R 0 can be further sub-sampled to create a mask R 1 which will be one-fourth the size of R in each dimension (Gibson et al. 2004).

8 130 Angiogram video sequence Meta data Video data sequence ROI detection Frames separation Get a frame Wavelet decomposition Directional decomposition Repositioning the WBCT coefficients Removal of insignificant coefficients SPECK encoding Are all frames completed? No Yes Compressed data Figure 5.1 Compression phase

9 131 Compressed data Meta data Video data Get compressed frame SPECK decoding Positioning the WBCT coefficients Inverse directional decomposition Inverse wavelet transform Has the total frame been achieved? No Yes Unification of frames Reconstructed angiogram sequence Figure 5.2 Decompression phase 5.4 SIMULATION RESULTS To demonstrate the effectiveness of the proposed method, it has been applied to 40 sets of real-time 8-bit angiogram video sequences. The results are presented on 3 level subband decomposition using biorthogonal

10 132 wavelet CDF 9/7 and a directional decomposition using DFB proposed by Do and Vetterli (2005).The number of directional subbands at finest scale is Objective Results The results of the objective performance measures discussed in Chapter 1 calculated for individual frames and summed for an angiogram sequence is given in rows of Table The table shows the RMSE, PSNR, CC, PRD and SSIM performance of the approach functioning at different bitrates. The structural similarity index measure indicates that fine structure of arteries present in angiogram image is not disturbed by compression. Correlation coefficient measure indicates that the original and the decompressed images are correlated and the percent rate of distortion indicates that the distortion caused by the proposed method is less. The results also indicate that the proposed method could gain good CR in maintaining a high PSNR. The quality of the image sequence is optimum when the number of sorting pass = 9 and data is minimum when the number of sorting pass = 7. Table 5.1 Performance evaluation of the proposed technique with number of sorting pass = 7 Sequence Name Sequence Size CR BPP RMSE PSNR CC PRD SSIM XA XA XA XA XA XA XA XA XA XA

11 133 Table 5.2 Sequence Name Performance evaluation of the proposed technique with number of sorting pass = 8 Sequence Size CR BPP RMSE PSNR CC PRD SSIM XA XA XA XA XA XA XA XA XA XA Table 5.3 Performance evaluation of the proposed technique with number of sorting pass = 9 Sequence Name Sequence Size CR BPP RMSE PSNR CC PRD SSIM XA XA XA XA XA XA XA XA XA XA

12 Quality Evaluation by Cardiologists To measure the subjective performance of the proposed method a subjective evaluation on the reconstructed images is performed by experienced Cardiologists. A total of 25 different image sets were used. These images were graded by the Cardiologist using a scale shown in Figure The results shown in Table 5.4 were obtained from four Cardiologists when the compression method is operating at a bit-rate 0.4 bits/pixel. Table 5.4 Results of quality measure obtained from Cardiologists for reconstructed angiogram sequences JPEG 2000 Proposed Method Min Max Min Max Cardiologist Cardiologist Cardiologist Cardiologist Based on the results reported in Table 5.4 it is understood that the proposed method yields diagnostically good quality images. For all the experimented sequences of angiogram, the Cardiologists were able to identify the presence of diagnostic features apparently in proposed method compared to JPEG Consequently, the proposed compression method can be adopted for real-time angiogram compressions. A frame extracted from the experimented sequences compressed respectively at 0.8 bpp, 0.4 bpp, 0.2 bpp, 0.1 bpp and 0.07 bpp is depicted in Figure 5.3. The Figures shows that the proposed method could produce good visual quality images when coded at 0.8 bpp and 0.4 bpp.

13 135 (a) (b) (c) (d) (e) (f) Figure 5.3 A frame extracted from the experimented angiogram sequence encoded at different bit rates (a) Original angiogram frame (b) 0.8 bpp (PSNR = db) (c) 0.4 bpp (PSNR = db) (d) 0.2 bpp (PSNR= db) (e) 0.1 bpp (PSNR = db) (f) 0.07bpp (PSNR= db)

14 Comparative Results To demonstrate the compression performance of the proposed method it has been compared with the existing wavelet based coding schemes JPEG 2000 and Wavelet + SPIHT. All the coding schemes used the same image sequences and the results have been tabulated. From Figure 5.4 and Table 5.5 it is understood that the proposed method outperforms JPEG 2000 both in CR and PSNR and it outperforms the Wavelet + SPIHT in CR maintaining a similar PSNR. From Table 5.4 it is clear that the proposed method is better than JPEG 2000 in producing good visual quality and diagnostically efficient images. Hence, the reconstructed images obtained from proposed method can be accepted for medical diagnosis JPEG 2000 CR JPEG 2000 PSNR Wavelet+SPIHT CR Wavelet+SPIHT PSNR Proposed Method CR Proposed Method PSNR 0 XA1 XA2 XA3 XA4 XA5 XA6 XA7 XA8 XA9 XA10 Sequence Names Figure 5.4 Comparison of CR and PSNR obtained using JPEG 2000, Wavelet + SPIHT and proposed method for angiogram sequences

15 137 It is valuable mentioning that the proposed coder because of its simplicity is time saving compared to the existing methods. Table 5.6 provides the coding and decoding time of experimented angiogram sequence with 16 frames. On the basis of the results reported in Table 5.6, it is clear that on an average for all bit rates, the proposed method s encoding time is nine times and decoding time is 19 times less than 3-D SPIHT and the proposed method s encoding time is three times and the decoding time is two times less than the Wavelet + SPIHT. Table 5.5 Comparison of CR and PSNR obtained using JPEG 2000, Wavelet + SPIHT and the proposed method for angiogram sequences Sequence Name JPEG 2000 Wavelet+SPIHT Proposed Method CR PSNR CR PSNR CR PSNR XA XA XA XA XA XA XA XA XA XA

16 138 Table 5.6 Encoding and decoding time of 3-D SPIHT, Wavelet + SPIHT and the proposed method (for 16 frames in an angiogram sequence) in seconds Bit Rate Encoding Time 3-D SPIHT Wavelet+SPIHT Proposed Method Decoding Time Encoding Time Decoding Time Encoding Time Decoding Time CONCLUSION In this chapter, a simple and effective compression method for coronary angiogram image sequences has been presented. The method is based on the fact that some areas of an angiogram sequence contains less significant information and can be efficiently removed in the contourlet representation without disturbing the diagnostic quality. The ability of contourlet transform in representing contours in an efficient way is the motivation for selecting it for representing the coronary angiogram images which contains numerous smooth edges. The evaluation of the compression results is first performed by measuring the image quality using the RMSE, PSNR, CC, PRD and SSIM and then using trained cardiologists to evaluate the amount of loss of diagnostic information and finally using the computation time. Overall, the results are hopeful and the proposed method as such can be used in real-time angiogram compressions.

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