M.Sc. Engg.http://hdl.handle.net/20.500.12228/562024-03-28T08:54:12Z2024-03-28T08:54:12ZA Technique for Assuring Secrecy and Lossless Properties of Digital ImageTanveer, Md. Siddiqur Rahmanhttp://hdl.handle.net/20.500.12228/9032021-09-08T05:01:06Z2020-09-01T00:00:00ZA Technique for Assuring Secrecy and Lossless Properties of Digital Image
Tanveer, Md. Siddiqur Rahman
Due to various disease diagnosis, the volume of medical data is rising fast. Also, for telemedicine, while medical image transmits over the public network, the distortion of pixels may cause erroneous disease diagnosis. Here, encryption of the image by multiple chaos-based schemes along with DNA cryptography can be a safeguard. As chaotic schemes are very sensitive to the initial conditions, a small difference in the initial conditions yields entirely uncorrelated sequences that assure the strength of encryption. To get high randomness, several DNA encoding and computing rules are deployed. This thesis proposes a multi-stage chaotic encryption technique for the medical image through Logistic map along with Lorenz attractor and DNA cryptography, where both schemes possess the most significant value of control parameters. Thus, their consecutive deployment generates colossal chaotic sequences that ensure the robustness of the proposed technique. At first, the usage of the Logistic map with SHA-256 hash value generates a chaotic sequence that converts the plain medical image into a confusing image. Now, this sequence is used to create a confusion key to encrypt this blur image. Later on, to overcome the limitations of DNA computing rules and to get high randomness, encode this blur image and Lorenz attractor based key according to DNA encoding rules. These rules are determined randomly from eight encoding rules. Then, execute DNA operations between encoded blur image and Lorenz key using the four DNA computing rules and these rules are also determined by chaotic logistic sequence. Thus, the ultimate cipher is generated. Then, to approve the potency of the cipher, a randomness test according to NIST, security and statistical analyses and comparisons are performed.
This thesis is submitted to the Department of Computer Science and Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, September 2020.; Cataloged from PDF Version of Thesis.; Includes bibliographical references (pages 42-47).
2020-09-01T00:00:00ZAutomatic Detection and Classification of Diabetic Lesions for Grading Diabetic Retinopathy Using Fuzzy Rule-Based Classification SystemAfrin, Rubyahttp://hdl.handle.net/20.500.12228/9022021-01-12T21:00:13Z2020-10-01T00:00:00ZAutomatic Detection and Classification of Diabetic Lesions for Grading Diabetic Retinopathy Using Fuzzy Rule-Based Classification System
Afrin, Rubya
Diabetic Retinopathy (DR) is a chronic, progressive retinal disease which is the most common cause of legal blindness. The disease is threatening to eyes as it shows no signs of visual abnormality at the initial stage. It gradually decreases patient’s eye-sight and drives into blindness in future. Hence, the early detection of DR is vital to prevent the complete vision loss of diabetes patients. Traditional diagnosing system of DR requires quite trained ophthalmologists for monitoring the retina periodically. Moreover, several physical tests like fluorescein angiography, visual acuity test, and ocular coherence tomography are involved to detect DR which also require a lot of time to process. In this paper, a fuzzy rule-based classification technique is proposed for automatic detection and classification of retinal lesions for grading DR. The proposed technique consists of preprocessing of fundus images, extraction of candidate retinal lesions, formulation of feature set, and classification of DR. In the preprocessing phase, the technique eliminates background noises and extracts optic disc from the retinal fundus image. Four leading lesions; blood vessels, microaneurysms, haemorrhages, and exudates are extracted using different image processing techniques and two textural features; contrast and homogeneity are calculated in the detection phase. Then, these six input features; blood vessels area, microaneurysms count, haemorrhages area, exudates area, contrast and homogeneity are fed to fuzzy if-then rule-based classifier for predicting normal, mild NPDR, moderate NPDR, severe NPDR and PDR stages of DR.. A total of 520 retinal fundus images are collected from four public database; STARE, DIARETDB0, DIARETDB1 and MESSIDOR and the images are successfully classified by the fuzzy rule-based classifier with accuracy up to 92.42%. The sensitivity and specificity of the classifier are 92.44% and 94.29% respectively. The simulation result on publicly standard image datasets exhibits that the intended technique gives promising results in identifying retinal lesions and it has better capability of classifying several stages of DR compared with other existing automatic diagnosing system.
This thesis is submitted to the Department of Computer Science and Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, October 2020.; Cataloged from PDF Version of Thesis.; Includes bibliographical references (pages 52-59).
2020-10-01T00:00:00ZA Study on the Effect of Seam, Color and Boundary Priors on Salient Region DetectionIslam, Aminulhttp://hdl.handle.net/20.500.12228/9002020-11-11T21:00:17Z2019-12-01T00:00:00ZA Study on the Effect of Seam, Color and Boundary Priors on Salient Region Detection
Islam, Aminul
Being the best creation of almighty Allah, human beings have the ability to analyse a visual scenario. They not only see an image, but can judge the importance of different parts of a visual area. They can easily differentiate a running car from its background, can tell the color of different objects in an image and can focus attention to some important parts of a visible scene. The value of this visual power of human is easily understood if one thinks about these. Achieving this wonderful human quality using machine is one of the most precious goals of today’s scientific researches. With the continuous advancement of imaging technologies, more and more visual data are being collected all over the world. But, a major portion of these data are left unprocessed. Image processing is used to process these types of visual data. For all image processing techniques, the initial goal is to find some target region for extracting information from the image. Saliency detection is the technique of computationally finding important regions of an image. It is usually done using the contrast information present in an image. Seam map is the combination of cumulative summation of energy values from different directions. In this thesis, a combined method is proposed which uses seam importance map along with boundary aware color importance map. Color importance map is the weighted average of different color channels of Lab color space. Some intermediate combinations which are closer to the proposed optimized version but differ in the optimization technique are also presented in this thesis. Several standard benchmark datasets including the famous MSRA 10k and ECSSD datasets are used to evaluate performance of the suggested method. The proposed saliency model has been compared with several state of the art methods for each dataset. The qualitative and quantitative results from those omparisons make things easier to understand. Besides that, comparison with those state of the art techniques and precision recall curves and F-beta values found from the experiments on several datasets prove the superiority of the proposed method. This robust saliency model can
improve almost all types of vision related applications including object detection, robotics, medical image analysis, content aware image resizing etc. In this research, the proposed architecture of saliency detection technique has been applied into a simple implementation of pedestrian detection. Its application has significantly improved the result of pedestrian detection in terms of performance and accuracy.
This thesis is submitted to the Department of Computer Science and Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, December 2019.; Cataloged from PDF Version of Thesis.; Includes bibliographical references (pages 57-62).
2019-12-01T00:00:00ZProtein Folding Optimization in a Hydrophobic-Polar Model for Predicting Tertiary Structure Using Fruit Fly Optimization AlgorithmChatterjee, Sajibhttp://hdl.handle.net/20.500.12228/8972020-11-11T21:00:13Z2020-02-01T00:00:00ZProtein Folding Optimization in a Hydrophobic-Polar Model for Predicting Tertiary Structure Using Fruit Fly Optimization Algorithm
Chatterjee, Sajib
The prediction of the three-dimensional structure of a protein from its amino acid sequence is an experiment that is very much well known optimization problem which is known as the Protein Folding Optimization (PFO) in many years. The PFO problem states to the computational problem of how to predict the local structure of a protein from its amino acid.
PFO problem is the NP-hard and most challenging problem. Various kind of optimization algorithm already applied for solving the PFO problem, but none of the existing algorithm not provide the accurate result within optimal time. Fruit Fly Optimization Algorithm (FOA)
is a recent metaheuristics algorithm that have the intensity and diversity characteristics of searching technique. Therefore, we applied FOA for solving PFO problem in the HP (Hydrophobic-Polar) cubic lattice model. In order to increase the convergence of the FOA, we have designed and developed three different operators of FOA: smell-based search, local vision-based search and global vision-based search technique for the perspective of PFO problem. The proposed algorithm is based on two extra mechanisms centroid hydrophobic and moderator mechanism, which are accountable for improving the accomplishment of the algorithm. The centroid hydrophobic mechanism tries to move the hydrophobic monomers to the center position of the structure. The moderator mechanisms try to move a part of monomers in the protein sequence each possible directions and place at the position where the maximum energy value found. This two extra mechanisms improved the performance of the propose algorithm magically. Moreover, we have developed a reconstruction operator for producing an accurate 3D structure of protein sequences by erasing overlapping in cubic lattice points. The experiment result shows of our proposed Fruit Fly Optimization Algorithm for Protein Folding Optimization (PFO_FOA) provide better accuracy than the existing algorithms.
This thesis is submitted to the Department of Computer Science and Engineering, Khulna University of Engineering & Technology in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Engineering, February 2020.; Cataloged from PDF Version of Thesis.; Includes bibliographical references (pages 59-64).
2020-02-01T00:00:00Z