## Rekha Haridoss* and Samundiswary Punniyakodi*## |

HMCR_{MAX} | HMCR_{MIN} | HMS | PAR_{MAX} | PAR_{MIN} | BW_{MAX} | BW_{MIN} | NI |

0.95 | 0.4 | 150 | 0.8 | 0.4 | 0.5 | 0.2 | 100 |

Table 2.

Differential weight (F) | Crossover probability (CR) | Population size (NP) | Lower and upper bound |

0.5 | 0.95 | ≥4 | [0, 255] |

To justify the performance of the proposed algorithm, different combinations of the entropies are incorporated with the proposed (OIHSA) and the existing (DE, HAS, and IHSA) image compression techniques. For demonstrating purpose, the CT and MRI medical images are considered. For a valuable comparison, the performance metrics such as PSNR, SSIM and computation time are considered respectively. Here, the compression ratio is fixed as 2.6. Table 3 shows the comparison of different optimization based image compressions for different entropies with respect to the average computation time. From observations, the proposed OIHSA based image compression using Shannon entropy have significantly lesser computation time than that of the other algorithms. Also, it is clearly visible that the overall computation time of the HMT using OIHSA with the combination of different entropies took very lesser time than other existing techniques. Though the computation time of the image compression using HSA stood a second place in the comparison table, the PSNR and SSIM of the HSA are not good. It is noted from Table 4 that the PSNR of the proposed image compression using OIHSA is nearly 2 dB better than the IHSA and far better than that of the other compression algorithms. Further, the quality of the images are mainly determined by the SSIM value, if it is close to 1, then the compressed image is almost equal to the input image. It is inferred through the SSIM results from Table 5, that the proposed algorithm has high SSIM than that of the other compression algorithms. It is also noted from the Tables 3–5, that the optimization methods used in HMT based image compression shown not much difference in PSNR and SSIM values for fuzzy, Renyi, and Tsallis entropies. But, there is a huge difference when it comes to the proposed method with the Shannon entropy combination. Moreover, the overall performance of the HSA and DE based image compressions produce worst results compared to that of the other techniques. Apart from the computation time, the performance of the HSA is poorer than IHSA. Hence, IHSA based image compression is only considered and HSA combination is excluded for further analysis.

Table 3.

Entropy | DE [23] | HSA | IHSA | OIHSA | ||||

CT | MRI | CT | MRI | CT | MRI | CT | MRI | |

Shannon | 7.08 | 7.68 | 1.654 | 1.697 | 1.713 | 1.81 | 1.41 | 1.47 |

Fuzzy | 73.1 | 31.89 | 1.807 | 2.22 | 2.357 | 1.78 | 1.53 | 1.62 |

Renyi | 10.5 | 14.04 | 1.81 | 1.87 | 2.86 | 1.74 | 1.51 | 1.58 |

Tsallis | 24.21 | 50.32 | 8.562 | 1.91 | 3.86 | 1.82 | 1.60 | 1.67 |

Table 4.

Entropy | DE [23] | HSA | IHSA | OIHSA | ||||

CT | MRI | CT | MRI | CT | MRI | CT | MRI | |

Shannon | 37.75 | 37.52 | 34.49 | 40.10 | 41.90 | 41.58 | 43.61 | 42.19 |

Fuzzy | 34.17 | 30.73 | 36.75 | 39.74 | 40.25 | 39.02 | 40.87 | 40.46 |

Renyi | 38.91 | 37.73 | 36.65 | 39.37 | 40.79 | 37.89 | 40.86 | 40.57 |

Tsallis | 37.78 | 20.36 | 36.4 | 37.57 | 41.09 | 38.79 | 41.52 | 39.02 |

Table 5.

Entropy | DE [23] | HSA | IHSA | OIHSA | ||||

CT | MRI | CT | MRI | CT | MRI | CT | MRI | |

Shannon | 0.938 | 0.946 | 0.915 | 0.981 | 0.977 | 0.987 | 0.990 | 0.990 |

Fuzzy | 0.983 | 0.891 | 0.927 | 0.975 | 0.966 | 0.975 | 0.972 | 0.986 |

Renyi | 0.946 | 0.968 | 0.925 | 0.963 | 0.976 | 0.956 | 0.975 | 0.979 |

Tsallis | 0.987 | 0.81 | 0.991 | 0.944 | 0.978 | 0.958 | 0.987 | 0.981 |

The performance of the HMT using different entropies and optimization algorithms is also carried out in terms of the PSNR and computation time by increasing the compression ratio. For analyzing the algorithms for different compression ratio, CT image is considered. It is noted from Fig. 2, for all optimization, as the compression ratio increases, the computation time decreases drastically. From Fig. 2(a), the HMT using DE and Shannon have lesser computation time than other entropy combinations such as fuzzy, Renyi, and Tallis entropies. But, the Shannon combination itself took more time to compute the DE based image compression algorithm. The HMT based image compression with the combination of IHSA and different entropies shown in Fig. 2(b) have better computation time than DE. For the proposed OIHSA based image compression using Shannon entropy illustrated in Fig. 2(c), indeed needs very less computation time than the previous methods.

Overall, from the different entropies, Shannon entropy stood a better place when it combined with the HMT based image compression using OIHSA. Moreover, the results from Fig. 3(a) illustrated that the proposed OIHSA using Shannon entropy based compression has very less computation time for different compression ratio compared to that of the existing techniques. The medical-based applications mostly concentrate on the quality of the medical image for further diagnosis. Hence, the comparison of the different optimization based image compression algorithms are also done by using PSNR. For better results with high PSNR, the compression ratio is taken up to 3. It is noted from Fig. 3(b), that the OIHSA based image compression using Shannon entropy has high PSNR and shown very good performance with high compression ratio.

The effect of the proposed compression technique with different entropies is also investigated in terms of varying the number of thresholds. From Fig. 4, it is noted that the selection of number of thresholds always decides the amount of compression ratio. The minimum limit for choosing the number of thresholds is 16. Because, if it is less than 16 then there is no guaranty for the perfect reconstructed image and the compressed image would be lost most of their fine details. Hence, it is safer to choose the number of thresholds more than 40 for medical images.

5.2 Qualitative AnalysisIn the telemedicine field, medical images are the important inputs in determining the medical diagnosis and treatment. Particularly, during medical image acquisition, compression and display, there are many parameters involved and that have an impact on the observer’s ability to make sure that diagnosis has done accurately. From the development and evaluation of the medical imaging systems, one always wishes to adjust the relevant system parameters to optimize observer performance. Likewise, an image acquisition, pre-processing, and display methods are developed, an assessment regarding the efficacy of the potential innovations is often desired. These kinds of determinations are based upon an assessment of the “quality” of the images. Thus the quality of the image is the major part to diagnose the disease for further treatment.

Generally, the estimation of the performance metrics such as PSNR and IQI is not enough to analyze the efficiency of the compressed output. Therefore, visual inspections are necessary to judge the amount of recovered information and artifacts. The qualitative comparison of the proposed method with different entropies is shown in Figs. 5 and 6. From the figures, the visual quality of the Tsallis entropy combination is very poor when compared with the other combinations. It is also observed that the proposed algorithm with the combination of the Shannon entropy has more details and have sharp edge details than other entropy combinations. Although, the performance of the fuzzy entropy combination is good, it fails to recover the fine details or features when the compression rate increases.

In this paper, a novel HMT based image compression algorithm is developed by appending OIHSA with different entropies. By applying the dynamically varying BW as a selection parameter and extracting the best threshold values from each region of the image histogram, efficient image compression was achieved. Further, the overall computation time of the proposed compression method is reduced to some extent by using opposition based learning concept. The various performance metrics such as PSNR, SSIM and computation time of the proposed method are determined and compared with the existing optimization based compression techniques. Also, the visual quality of the output images are taken into account for comparison. The combination of HMT based image compression with OIHSA overcomes the limitations of high computational time and compression ratio at a limited memory space for medical based digital images. Although, the compression introduced by the proposed OIHSA with different entropies reduces the computational time to a great extent, it is observed from the simulation that the proposed algorithm with Shannon entropy undisputedly performs well. This work can be extended further by hybridizing the different optimization techniques to improve the local search of OIHSA and also to improve the performance of the compression algorithm with respect to the PSNR and SSIM.

She received her B.E. degree in Electronics and Communication Engineering from Bharadidasan University, Tamilnadu, India in 2004 and M.Tech. degree in Electronic and Communication from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 2010. She is a student member of IEEE and IET. She worked as Assistant Professor in private engineering college, Pondicherry, India. At present, she is pursuing her Ph.D. degree in the Department of Electronics Engineering from Pondicherry University, Pondicherry, India. Her current research includes image processing, wireless multimedia sensor network. Compression and Enhancement of Medical Images Using Opposition Based Harmony Search Algorithm

She received her B.Tech. and M.Tech. degrees in Electronics and Communication Engineering from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 1997 and 2003, respectively. She received her Ph.D. degree from Pondicherry Engineering College affiliated to Pondicherry University, Pondicherry, India in 2011. She has been working in teaching profession since 1998. Presently, she is working as Assistant Professor in the Department of Electronics Engineering, School of Engineering and Technology, Pondicherry Central University, India. She has nearly 18 years of teaching experience. She has published more than 70 papers in national and international conference proceedings and journals. She has co-authored a chapter of the book published by INTECH Publishers. She has been one of the authors of the book published by LAMBERT Academic Publishing. Her area of interest includes wireless communication and networks, wireless security and computer networks.

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