Study on Statistics Iterative Thresholding Segmentation Based on Aviation Image

Yang, Guang; Chen, Kexiong; Zhou, Mei; Xu, Zhonglin; Chen, Yongtian

doi:10.1109/snpd.2007.512

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…These methods are efficient and suitable only for medical images of high resolution and good contrast and they do not perform well for segmentation of images with multiple objects each having distinct gray level value varying over a band of values [6]. Whereas, local thresholding or adaptive thresholding approach determines the threshold value based on local statistics and threshold value found is locally optimal for small areas [7]. But these techniques are iterative based and time complexity is high.…”

Section: Introductionmentioning

confidence: 99%

Automatic Roi Extraction in Noisy Medical Images

Renukalatha¹,

Kalyanasundaram²

2017

IJIVP

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Accurate segmentation of medical images is pivotal in medical image analysis as it favors the detection and quantification of abnormalities present in human anatomical structures. Since medical images are complex and sometimes noisy, effective extraction of the regions of abnormalities is a tedious process. Many semi-automatic segmentation algorithms with appreciable segmentation accuracy do exist in literature. However, these techniques are iterative, computationally expensive, involve human intervention demanding initial parameter settings and moreover, each one of them is specific to a particular modality. In addition, presence of noise further degrades the quality of the processed image. There is no general algorithm to extract the key regions from all types of noisy medical images. This paper proposes an automatic Region of Interest (ROI) extraction algorithm to detect the important regions in noisy medical images of different modalities using statistical moments. The proposed approach estimates an optimal threshold value automatically using statistical moments through histogram decomposition technique. Initially, the medical image database is preprocessed followed by ROI extraction and the performance of the proposed approach is compared with other techniques to verify its robustness.

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Section: Introductionmentioning

confidence: 99%

Automatic Roi Extraction in Noisy Medical Images

Renukalatha¹,

Kalyanasundaram²

2017

IJIVP

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“…These techniques have the aim to reduce the number of color components from the input image. The main characteristics used as the basis of color image thresholding are color and texture [5]. Color histograms are basically used in content-based healing systems and have demonstrated to be convenient.…”

Section: Introductionmentioning

confidence: 99%

Modified Image Thresholding Algorithm using Social Impact Theory Optimization (SITO)

Kaur¹,

Mittal²

2014

IJCA

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Thresholding is considered as pivotal tool for image segmentation [1]. The main aim of thresholding is to divide the pixels into different groups in a logical way [2]. One of the most suitable algorithm for thresholding is Social Impact Theory Based Optimization (SITO).Social Impact theory optimization algorithm has been considered as one of the important technique to find the better optimized results as it is based on human behavior. The cross entropy function works well in case of bi-level thresholding problem. However, if there is a need of the multi-thresholding in image processing application, a global and generic objective function is desired so that each threshold could be tested for its best performance statistically [6]. The maxima of the selected threshold are optimized by using the SITO algorithm based on maxima of sum of entropy ad standard deviation. The results are compared with negative selection algorithm (NSA) which is artificial intelligence (AI) technique, maximum entropy algorithm and OTSU algorithm. The performance measures i.e. Standard Deviation, Entropy, MSE and PSNR prove the improvements of SITO based thresholding.

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“…The challenges for automatic segmentation of the MRI head images have given rise to many different approaches. The techniques of segmentation developed so far include statistical pattern recognition techniques [2,3,4], morphological processing with thresholding [5,6], clustering algorithm [7] and active contour [8,9]. In this paper, we segment the intracranial area into 2 clusters which are abnormal regions, CSF and brain matter.in this paper a novel fuzzy OTSU method is used for brain tumor detection.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Fuzzy Technique for Detection of Brain Tumor

Sirisha¹,

Raju²,

Reddy³

2013

IJCT

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Â Â In this epoch Medical Image segmentation is one of the most challenging problems in the research field of MRI scan image classification and analysis. The importance of image segmentation is to identify various features of the image that are used for analyzing, interpreting and understanding of images. Image segmentation for MRI of brain is highly essential due to accurate detection of brain tumor. This paper presents an efficient image segmentation technique that can be used for detection of tumor in the Brain. This innovative method consists of three steps. First is Image enhancement to improve the quality of the tumor image by eliminating noise and to normalize the image. Second is fuzzy logic which produce optimal threshold to avoid the fuzziness in the image and makes good regions regarding Image and tumor part of the Image. Third is novel OTSU technique applied for separating the tumor regions in the MRI. This method has produced better results than traditional extended OTSU method.

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Study on Statistics Iterative Thresholding Segmentation Based on Aviation Image

Cited by 11 publications

References 9 publications

Automatic Roi Extraction in Noisy Medical Images

Automatic Roi Extraction in Noisy Medical Images

Modified Image Thresholding Algorithm using Social Impact Theory Optimization (SITO)

An Efficient Fuzzy Technique for Detection of Brain Tumor

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