Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Binary segmentation, a problem of extracting foreground objects from the background, often arises in medical imaging and document processing. Popular existing solutions include expectation maximization (EM) algorithm, Otsu thresholding, K-sigma thresholding, and the recently proposed generalized principal component analysis (GPCA).  apply these algorithms to segmentation of noisy images with small foreground objects. Such images often arise in change detection applications such as functional magnetic resonance imaging (fMRI). In our experiments none of the algorithms performed sufficient ll when the total size of foreground regions was much smaller than the size of the background region.  propose a novel algorithm, called sGPCA, that can robustly estimate the intensity of small foreground objects in the presence of noise. The intensity estimate obtained can be used to determine an optimal threshold value or to initialize EM and Markov random field (MRF) based segmentation algorithms.
['Rashi Samur', 'Vitali Zagorodnov']
I am writing a research paper on a idea Segmenting small regions in the presence of noise
[{"from": "human", "value": "I am writing a research paper on a idea Segmenting small regions in the presence of noise"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Binary segmentation, a problem of extracting foreground objects from the background, often arises in medical imaging and document processing. Popular existing solutions include expectation maximization (EM) algorithm, Otsu thresholding, K-sigma thresholding, and the recently proposed generalized principal component analysis (GPCA).  apply these algorithms to segmentation of noisy images with small foreground objects. Such images often arise in change detection applications such as functional magnetic resonance imaging (fMRI). In our experiments none of the algorithms performed sufficient ll when the total size of foreground regions was much smaller than the size of the background region.  propose a novel algorithm, called sGPCA, that can robustly estimate the intensity of small foreground objects in the presence of noise. The intensity estimate obtained can be used to determine an optimal threshold value or to initialize EM and Markov random field (MRF) based segmentation algorithms."}]