New design of adaptive Gabor wavelet filter bank for medical image retrieval

Samantaray, Aswini K.; Rahulkar, Amol D.

doi:10.1049/iet-ipr.2019.1024

Cited by 13 publications

(5 citation statements)

References 33 publications

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“…The saliency map of image color features is obtained by calculating the color distance and spatial distance weighting of the super pixel region. When extracting texture features from images, Gabor filters are used [17][18][19]. Firstly, a Gabor filter is designed that includes 24 Gabor filters that can be tuned to different frequencies and directions.…”

Section: ( )mentioning

confidence: 99%

The Application of FLICSP Algorithm Based on Multi-Feature Fusion in Image Saliency Detection

Li,

Wu,

Wang

2024

IEEE Access

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To raise the performance of the current image saliency detection method and promote the extraction effect of salient regions in complex images, the color and texture features fusion is studied on the basis of fast linear iterative clustering with active search to obtain the relevant saliency detection algorithm, and then the fused saliency map is obtained. Algorithm performance is optimized by using a deep prior information-assisted image enhancement model. The outcomes express that compared with other algorithms, the improved algorithm has a smaller mean absolute error, higher precision and F value, lower missed detection rate and false detection rate, and higher peak signal-to-noise ratio and structure similarity index. In the JUDD dataset, the minimum mean absolute error value of the improved algorithm is 0.143, which is 0.34 less than the original algorithm. The improved algorithm in the PASCAL dataset has the highest precision, and F-value, with 0.786 and 0.754 respectively, while the F-value of the pre-improved algorithm is 0.678. In terms of missed detection rate, the improved algorithm is 4.7%, which is 2.5% lower than the previous algorithm; In terms of false detection rates, the pre and post-improvement algorithms have false detection rates of 3.2% and 5.1%, respectively. In the peak signal-to-noise ratio index, the improved algorithm has a maximum value of 39.45 dB, which is 6.82 dB higher than the previous algorithm; Unlike other algorithms, the improved algorithm has the highest similarity index value of 0.892. Research methods can effectively detect the saliency of complex images.

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Section: ( )mentioning

confidence: 99%

The Application of FLICSP Algorithm Based on Multi-Feature Fusion in Image Saliency Detection

Li,

Wu,

Wang

2024

IEEE Access

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“…Measures uniformity in grey level distribution of an image [74,188] Entropy Measures texture complexity [189] Contrast Measures difference in grey levels of an image [190] Homogeneity Measures similarity between pairs of pixels in an image [147] Correlation Measures statistical relationship between pairs of pixels in an image [74] Gabor filters Frequency Number of cycles of a pattern present in an image [191] Orientation Angle at which a pattern is oriented in an image [192] Scale Size of the pattern present in an image [147] Local Binary Patterns (LBP)…”

Section: Energymentioning

confidence: 99%

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

et al. 2023

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Conventional measurement methods for above-ground biomass (AGB) are time-consuming, inaccurate, and labor-intensive. Unmanned aerial systems (UASs) have emerged as a promising solution, but a standardized procedure for UAS-based AGB estimation is lacking. This study reviews recent findings (2018–2022) on UAS applications for AGB estimation and develops a vegetation type-specific standard protocol. Analysis of 211 papers reveals the prevalence of rotary-wing UASs, especially quadcopters, in agricultural fields. Sensor selection varies by vegetation type, with LIDAR and RGB sensors in forests, and RGB, multispectral, and hyperspectral sensors in agricultural and grass fields. Flight altitudes and speeds depend on vegetation characteristics and sensor types, varying among crop groups. Ground control points (GCPs) needed for accurate AGB estimation differ based on vegetation type and topographic complexity. Optimal data collection during solar noon enhances accuracy, considering image quality, solar energy availability, and reduced atmospheric effects. Vegetation indices significantly affect AGB estimation in vertically growing crops, while their influence is comparatively less in forests, grasses, and horizontally growing crops. Plant height metrics differ across vegetation groups, with maximum height in forests and vertically growing crops, and central tendency metrics in grasses and horizontally growing crops. Linear regression and machine learning models perform similarly in forests, with machine learning outperforming in grasses; both yield comparable results for horizontally and vertically growing crops. Challenges include sensor limitations, environmental conditions, reflectance mixture, canopy complexity, water, cloud cover, dew, phenology, image artifacts, legal restrictions, computing power, battery capacity, optical saturation, and GPS errors. Addressing these requires careful sensor selection, timing, image processing, compliance with regulations, and overcoming technical limitations. Insights and guidelines provided enhance the precision and efficiency of UAS-based AGB estimation. Understanding vegetation requirements aids informed decisions on platform selection, sensor choice, flight parameters, and modeling approaches across different ecosystems. This study bridges the gap by providing a standardized protocol, facilitating widespread adoption of UAS technology for AGB estimation.

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“…As Wavelet Features, we computed the Gabor [50] and Haar [51] wavelets. Gabor wavelet filter bank is an efficient tool for the analysis of local time-frequency characteristics.…”

Section: Hand-crafted Image Descriptorsmentioning

confidence: 99%

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Loddo

Putzu

2021

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Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks.

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New design of adaptive Gabor wavelet filter bank for medical image retrieval

Cited by 13 publications

References 33 publications

The Application of FLICSP Algorithm Based on Multi-Feature Fusion in Image Saliency Detection

The Application of FLICSP Algorithm Based on Multi-Feature Fusion in Image Saliency Detection

An Overview of Using Unmanned Aerial System Mounted Sensors to Measure Plant Above-Ground Biomass

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

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