Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis

Chen, Chiao-Min; Chen, Chih‐Cheng; Wu, Ming‐Chi; Horng, Gwoboa; Wu, Hsien-Chu; Hsueh, Shih-Hua; Ho, His-Yun

doi:10.1007/s40846-015-0096-6

Cited by 54 publications

(19 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It also adjusts the initial pixel quantities with accuracy of 87.1% which increases the contrast between the images. The dynamic model on the pixel similarity functions for optimization is developed by Chiao et al [5]. MRI differentiation is implemented to region of interest.…”

Section: Related Workmentioning

confidence: 99%

“…The isolateral filtering technique is used in this research work which works directly on pixels taken into consideration of the neighbourhood pixel values depending on the intensity of the pixel values. It works in spatial domain [4], [5] to remove the noise vectors in brain tumor image. This filter primarily depends on the Gaussian distributed values and also the distance between subsequent pixel values with variable intensities.…”

Section: Algorithm 1: Isolateral Filtermentioning

confidence: 99%

See 1 more Smart Citation

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

DEEPA¹,

S²,

Geisa

2020

Preprint

View full text Add to dashboard Cite

Background: In recent days, the field of nano-technology is becoming popular due to increased rate of accurate detection and effectiveness in clinical patients using Computer-Aided Diagnosis (CAD). The high rate of precision with accuracy and classification of brain tumor as benign or malignant can be achieved with nano-scale imaging technology. This helps to enhance the superiority of life for brain tumor diseased patients. Results: In this work, we propose the novel Semantic nano-segmentation for the detection of brain tumors even in nano scale range. Proposed Semantic Nano-segmentation based on Advanced - Convolutional Neural Networks will help the radiologists to find the brain cancer even at early stages with nodules at very smaller size. The proposed method of Advanced - Convolutional Neural Networks (A-CNN) uses ResNet-50. Here the nano-image is taken as input and tumor image is segmented using Semantic Nano-segmentation which carries an average dice and SSIM values to be 0.2133 and 0.9704 respectively. The accuracy of 93.2% and 92.7% is obtained by the proposed Semantic nano segmentation for benign and malignant tumor images respectively. A-CNN method of automatic classification has an average accuracy of 99.57% and 95.7% for benign and malignant images respectively. Conclusion: This novel nano-method is created for effective detection of tumor area in nanometers (nm) and thus evaluates the disease perfectly. Closeness of the Proposed method at ROC curve with reference to True Positive values indicates higher performance than other methods. Comparative analysis on ResNet-50 with testing and training data at rate of 90% -10%, 80%-20% and 70%-30% respectively is made which proves the effectiveness of the proposed work.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Algorithm 1: Isolateral Filtermentioning

confidence: 99%

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

DEEPA¹,

S²,

Geisa

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, the median filter [31] is applied to reduce the ambiguity between a bone structure and fracture, as shown in Figure 13a. Then, the automatic contrast enhancement [32] is implemented to obtain a clear fragment separation in bone structure, as shown in Figure 13b. The image histogram, which shows the relationship between the gray level and the corresponding frequency, can be expressed as:…”

Section: Build Contour Hierarchiesmentioning

confidence: 99%

Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images

Rahmaniar

Wang

2019

Applied Sciences

View full text Add to dashboard Cite

Calcaneus fractures often occur because of accidents during exercise or activities. In general, the detection of the calcaneus fracture is still carried out manually through CT image observation, and as a result, there is a lack of precision in the analysis. This paper proposes a computer-aid method for the calcaneal fracture detection to acquire a faster and more detailed observation. First, the anatomical plane orientation of the tarsal bone in the input image is selected to determine the location of the calcaneus. Then, several fragments of the calcaneus image are detected and marked by color segmentation. The Sanders system is used to classify fractures in transverse and coronal images into four types, based on the number of fragments. In the sagittal image, fractures are classified into three types based on the involvement of the fracture area. The experimental results show that the proposed method achieves a high precision rate of 86%, with a fast computational performance of 133 frames per second (fps), used to analyze the severity of injury to the calcaneus. The results in the test image are validated based on the assessment and evaluation carried out by the physician on the reference datasets.

show abstract

“…In preprocessing, contrast enhancement of multicenter data image is an important challenge. It was shown that a high-contrast medical image could lead to a better interpretation of the different adjacent tissues in the imaged body part [19,20]. Accordingly, the resulting enhanced image, which is in terms of signal intensities of different tissues, can facilitate the automated segmentation, feature extraction, and classification of these tissues.…”

Section: Related Workmentioning

confidence: 99%

Automatic Histogram Specification for Glioma Grading Using Multicenter Data

Chen

Zhao

et al. 2019

Journal of Healthcare Engineering

View full text Add to dashboard Cite

Multicenter sharing is an effective method to increase the data size for glioma research, but the data inconsistency among different institutions hindered the efficiency. This paper proposes a histogram specification with automatic selection of reference frames for magnetic resonance images to alleviate this problem (HSASR). The selection of reference frames is automatically performed by an optimized grid search strategy with coarse and fine search. The search range is firstly narrowed by coarse search of intraglioma samples, and then the suitable reference frame in histogram is selected by fine search within the sample selected by coarse search. Validation experiments are conducted on two datasets GliomaHPPH2018 and BraTS2017 to perform glioma grading. The results demonstrate the high performance of the proposed method. On the mixed dataset, the average AUC, accuracy, sensitivity, and specificity are 0.9786, 94.13%, 94.64%, and 93.00%, respectively. It is about 15% higher on all indicators compared with those without HSASR and has a slight advantage over the result of a manually selected reference frame by radiologists. Results show that our methods can effectively alleviate multicenter data inconsistencies and lift the performance of the prediction model.

show abstract

Automatic Contrast Enhancement of Brain MR Images Using Hierarchical Correlation Histogram Analysis

Cited by 54 publications

References 32 publications

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

Diagnosis of Brain Tumor Using Nano Segmentation and Advanced-cnn Classification

Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images

Automatic Histogram Specification for Glioma Grading Using Multicenter Data

Contact Info

Product

Resources

About