scholarly journals Optimal Threshold based Brain Image Fusion for Brain Cancer Detection using Firefly algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 2750-2759
Keyword(s):  
Image Fusion ◽  
Firefly Algorithm ◽  
Convergence Time ◽  
Neoplastic Disease ◽  
Scale Invariant ◽  
Data Set ◽  
Sift Algorithm ◽  
Fusion Data ◽  
Interval Type ◽  
Scale Invariant Feature

In this paper an attempt is made to diagnose brain disease like neoplastic disease, cerebrovascular disease, Alzheimer disease, fatal disease, Sarcoma disease by effective fusion of two images. Two images are fused in three steps: Step 1.Segmentation: The images are segmented on the basis of optimal thresholding; thresholds are optimized with natural inspired firefly algorithm by assuming fuzzy entropy as objective function. Image thresholding is one of the segmentation techniques which is flexible, simple and has less convergence time as compared to others. Step 2: the segmented features are extracted with Scale Invariant Feature Transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: Finally fusion rules are made on the basis of interval type-2 fuzzy (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark Image fusion data set and proved better in all measuring parameters.

scholarly journals An Improved Firefly Algorithm-Based 2-D Image Thresholding for Brain Image Fusion

2020 ◽  
Vol 14 (3) ◽  
pp. 60-96
Author(s):  
Srikanth M. V. ◽  
V. V. K. D. V. Prasad ◽  
K. Satya Prasad
Keyword(s):  
Image Fusion ◽  
Firefly Algorithm ◽  
Brain Diseases ◽  
Data Sets ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Image Thresholding ◽  
Fusion Data ◽  
Improved Firefly Algorithm ◽  
Interval Type

In this article, an attempt is made to diagnose brain diseases like neoplastic, cerebrovascular, Alzheimer's, and sarcomas by the effective fusion of two images. The two images are fused in three steps. Step 1. Segmentation: The images are segmented on the basis of optimal thresholding, the thresholds are optimized with an improved firefly algorithm (pFA) by assuming Renyi entropy as an objective function. Earlier, image thresholding was performed with a 1-D histogram, but it has been recently observed that a 2-D histogram-based thresholding is better. Step 2: the segmented features are extracted with the scale invariant feature transform (SIFT) algorithm. The SIFT algorithm is good in extracting the features even after image rotation and scaling. Step 3: The fusion rules are made on the basis of an interval type-2 fuzzy set (IT2FL), where uncertainty effects are minimized unlike type-1. The novelty of the proposed work is tested on different benchmark image fusion data sets and has proven better in all measuring parameters.

scholarly journals Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Yong Chen ◽  
Lei Shang ◽  
Eric Hu
Keyword(s):  
Image Matching ◽  
Geometric Feature ◽  
Data Sets ◽  
Feature Points ◽  
Matching Method ◽  
Scale Invariant ◽  
Data Set ◽  
Sift Algorithm ◽  
Delta Rule ◽  
Scale Invariant Feature

As for the unsatisfactory accuracy caused by SIFT (scale-invariant feature transform) in complicated image matching, a novel matching method on multiple layered strategies is proposed in this paper. Firstly, the coarse data sets are filtered by Euclidean distance. Next, geometric feature consistency constraint is adopted to refine the corresponding feature points, discarding the points with uncoordinated slope values. Thirdly, scale and orientation clustering constraint method is proposed to precisely choose the matching points. The scale and orientation differences are employed as the elements ofk-means clustering in the method. Thus, two sets of feature points and the refined data set are obtained. Finally, 3 * delta rule of the refined data set is used to search all the remaining points. Our multiple layered strategies make full use of feature constraint rules to improve the matching accuracy of SIFT algorithm. The proposed matching method is compared to the traditional SIFT descriptor in various tests. The experimental results show that the proposed method outperforms the traditional SIFT algorithm with respect to correction ratio and repeatability.

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

An Improved Face Recognition Algorithm Based on SIFT and LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1999-2004 ◽  
Author(s):  
Huai Ming Yang ◽  
Jin Guang Sun
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Local Similarity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Face Images ◽  
Calculation Results ◽  
Scale Invariant Feature ◽  
Global And Local

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

Comparison of Contour Based and Feature Based Tracking Methods for Control of Microbiorobots

2009 ◽  
Author(s):  
Dal Hyung Kim ◽  
Edward Steager ◽  
Min Jun Kim
Keyword(s):  
Small Error ◽  
Small Scale ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Tracking Method ◽  
Miniature Robots ◽  
A Cell ◽  
Feature Based ◽  
Scale Invariant Feature ◽  
Exact Positions

Miniature robots should be precisely controlled because of a small workspace and size of their shapes. Small error of control could lead to failure of tasks such as an assembly. Tracking is one of the most important techniques because control of a small scale robot is hard to accomplish without object’s motion information. In this paper, we compare the feature based and the region based tracking methods with microbiorobot. Invariant features can be extracted using Scale Invariant Feature Transfrom (SIFT) algorithm because microbiorobot is a rigid body unlike a cell. We clearly showed that the feature based tracking method track exact positions of the objects than region based tracking method when objects are close contacted or overlapped. Also, the feature based tracking method allows tracking of objects even though partial object disappears or illumination is changed.

Recent Advances in Corneal Imaging

2011 ◽  
pp. 271-287
Author(s):  
A. Elbita ◽  
R. Qahwaji ◽  
S. Ipson ◽  
T. Y. Ahmed ◽  
K. Ramaesh ◽  
...  
Keyword(s):  
3D Model ◽  
Visual Quality ◽  
Scale Invariant ◽  
Image Capture ◽  
Diagnostic Aid ◽  
Sift Algorithm ◽  
Scale Invariant Feature ◽  
Future Work ◽  
Corneal Imaging

This chapter details work with sequences of corneal images from a confocal microscope to develop enhancement methods to improve the visual quality of the images. Due to involuntary movements of the subject’s eye during image capture, the images suffer both lateral and longitudinal translations, and work is ongoing to attempt to register adjacent images in the sequence. Currently this registration uses an approach based on the Scale Invariant Feature Transforms (SIFT) algorithm. Registration is a necessary stage in the construction of a 3D model of the subject’s cornea for use as a diagnostic aid. The algorithms, results, progress and suggestions for future work are presented in this chapter.

An Image Stitching Algorithm Based on Histogram Matching and SIFT Algorithm

2017 ◽  
Vol 31 (04) ◽  
pp. 1754006 ◽  
Author(s):  
Jing Zhang ◽  
Guangxue Chen ◽  
Zhaoyang Jia
Keyword(s):  
Weighted Average ◽  
Image Stitching ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Process ◽  
Histogram Matching ◽  
Key Points ◽  
Illumination Changes ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

Image stitching among images that have significant illumination changes will lead to unnatural mosaic image. An image stitching algorithm based on histogram matching and scale-invariant feature transform (SIFT) algorithm is brought out to solve the problem in this paper. First, histogram matching is used for image adjustment, so that the images to be stitched are at the same level of illumination, then the paper adopts SIFT algorithm to extract the key points of the images and performs the rough matching process, followed by RANSAC algorithm for fine matches, and finally calculates the appropriate mathematical mapping model between two images and according to the mapping relationship, a simple weighted average algorithm is used for image blending. The experimental results show that the algorithm is effective.

scholarly journals MRI Images under the Optimized Registration Algorithm for Primary Open Angle Glaucoma Visual Path Damage

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bin Zhou ◽  
Min Chen
Keyword(s):  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Scale Invariant Feature Transform ◽  
Open Angle ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The Impact

To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.

scholarly journals Analisis Komparasi Pencocokan Pola Citra Jenis Ikan Mujair (Oreochromis Mossambicus) Menggunakan Algortima Scale Invariant Feature Transform Dengan Algoritma k-Nearest Neighbor

2021 ◽  
Vol 20 (1) ◽  
pp. 16-21
Author(s):  
Suhadi . ◽  
Rudi Budi Agung ◽  
Syamsul Bahri
Keyword(s):  
Fish Species ◽  
Nearest Neighbor ◽  
Sampling Error ◽  
Oreochromis Mossambicus ◽  
K Nearest Neighbor ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

Fish is a very large source of protein, by eating fish it can be healthier and participate in educating the nation's future generations, so it must be preserved. Fish is a food commodity that is easily available in Indonesia, and the price is also affordable. Tilapia fish (Oreochromis Mossambicus) is a popular consumption fish in Indonesia found in rivers, lakes, and lakes with a salt content of less than 0.05% for breeding. Tilapia fish is widely consumed by the public as a cheap and delicious fish that is often found in traditional and modern markets. This fish is often sold fresh or through the process of freezing (frozen). Previous research used the K-Nearest Neighbor (K-NN) algorithm and Image Processing to detect fish species using a smartphone. The purpose of this study was to analyze the comparison between the Scale Invariant Feature Transform (SIFT) Algorithm and the K-Nearest Neighbor (K-NN) Algorithm to determine the matching image patterns of Mujair fish species. The conclusion of this study is that the SIFT algorithm is the most accurate with a sampling error of 0.31% and the k-NN algorithm with a sampling error of 69.89%.

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