A review on image segmentation techniques

Pal, Nikhil R.; Pal, Sankar K.

doi:10.1016/0031-3203(93)90135-j

Cited by 3,031 publications

(1,324 citation statements)

References 122 publications

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“…First, seed points were placed within the axons to initiate a watershed-based segmentation (Beucher and Lantuejoul, 1979;Meyer and Beucher, 1990) of the ICS. The myelin was then segmented using an interactive profile-based boundary detection algorithm (Pal and Pal, 1993). After manual correction, the ECS was determined by subtracting the union of the axon and the myelin regions from the entire image space.…”

Section: Synthetic Axon Images and Optical Images Of Mouse Spinal Cordmentioning

confidence: 99%

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

et al. 2008

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Q-space imaging (QSI), a diffusion MRI technique, can provide quantitative tissue architecture information at cellular dimensions not amenable by conventional diffusion MRI. By exploiting regularities in molecular diffusion barriers, QSI can estimate the average barrier spacing such as the mean axon diameter in white matter (WM). In this work, we performed ex vivo QSI on cervical spinal cord sections from healthy C57BL/6 mice at 400MHz using a custom-designed uniaxial 50T/m gradient probe delivering a 0.6 µm displacement resolution capable of measuring axon diameters on the scale of 1 µm. After generating QSI-derived axon diameter maps, diameters were calculated using histology from seven WM tracts (dorsal corticospinal, gracilis, cuneatus, rubrospinal, spinothalamic, reticulospinal, and vestibulospinal tracts) each with different axon diameters. We found QSI-derived diameters from regions drawn in the seven WM tracts (1.1 to 2.1 µm) to be highly correlated (r 2 = 0.95) with those calculated from histology (0.8 to 1.8 µm). The QSI-derived values overestimated those obtained by histology by approximately 20%, which is likely due to the presence of extracellular signal. Finally, simulations on images of synthetic circular axons and axons from histology suggest that QSI-derived diameters are informative despite diameter and axon shape variation and the presence of intra-cellular and extra-cellular signal. QSI may be able to quantify nondestructively changes in WM axon architecture due to pathology or injury at the cellular level.

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Section: Synthetic Axon Images and Optical Images Of Mouse Spinal Cordmentioning

confidence: 99%

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

et al. 2008

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“…Segmentation is an important process to extract information from complex medical images. Segmentation has wide application in medical field [2,14,21,22].…”

Section: Som and Hsom Image Segementationmentioning

confidence: 99%

“…A tumor is a mass of tissue that grows out of control of the normal forces that regulates growth [22].. In India, totally 80,271 people are affected by various types of tumor (2007 estimates).. "Artificial Neural Networks (ANNs) are mathematical analogues of biological neural systems, in the sense that they are made up of a parallel interconnected system of nodes, called neurons.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

Logeswari¹,

Karnan²

2010

IJCTE

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Abstract-Image Segmentation is an important and challenging factor in the medical image segmentation. This paper describes segmentation method consisting of two phases. In the first phase, the MRI brain image is acquired from patients database, In that film artifact and noise are removed. After that Hierarchical Self Organizing Map (HSOM) is applied for image segmentation. The HSOM is the extension of the conventional self organizing map used to classify the image row by row. In this lowest level of weight vector, a higher value of tumor pixels, computation speed is achieved by the HSOM with vector quantization.

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“…Many algorithms have been developed to segment images using edge detection (10). One of the approaches presented in this paper utilizes second-derivative edge detection based on identifying the generated zero crossings.…”

Section: Cell Counting Using Second-order Edge Detectionmentioning

confidence: 99%

An image analysis‐based approach for automated counting of cancer cell nuclei in tissue sections

et al. 2003

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BackgroundSemiquantitative evaluation and manual cell counting are the commonly used procedures to assess positive staining of molecular markers in tissue sections. Manual counting is also a laborious task in which consistent objectivity is difficult to achieve. Recently, image analysis has been explored, but the studies reported were limited to histological images acquired at high magnification and containing uniformly stained cells.MethodsThe analyzed material consisted of histological sections from different squamous cell cancers that had stained for proliferation using Ki‐67 and cyclin A detection. The first step of the method was based on detecting the overall number of cells irrespective to their stain, using second‐order edge detection methodology. Then proliferating cells were located using principal component analysis (PCA) of the color image, combined with histogram thresholding.ResultsThe algorithms' performances were validated on tissue section images encountered in routine clinical practice by comparison with objective measures of performance and manual cell identification. The algorithms correlated closely with manual counting of all cells (r2 = 0.96–0.97) and stained cells (4–7% cell count error).ConclusionsCell counting in complex large‐scale histological images could be applied in routine practice using edge and color information. The proposed technique provides several benefits, such as speed of analysis, consistency, and automation. Moreover, it is faster than human observation and could replace the laborious task of manual cell counting. Cytometry Part A 55A:30–42, 2003. © 2003 Wiley‐Liss, Inc.

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A review on image segmentation techniques

Cited by 3,031 publications

References 122 publications

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

Indirect measurement of regional axon diameter in excised mouse spinal cord with q-space imaging: Simulation and experimental studies

An Enhanced Implementation of Brain Tumor Detection Using Segmentation Based on Soft Computing

An image analysis‐based approach for automated counting of cancer cell nuclei in tissue sections

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