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Heidari, Marjan, Taghizadeh, Mehdi, Masoumi, Hassan, Valizadeh, Morteza. (1400). Liver Segmentation in MRI Images using an Adaptive Water Flow Model. سامانه مدیریت نشریات علمی, 11(4), 527-534. doi: 10.31661/jbpe.v0i0.2103-1293
Marjan Heidari; Mehdi Taghizadeh; Hassan Masoumi; Morteza Valizadeh. "Liver Segmentation in MRI Images using an Adaptive Water Flow Model". سامانه مدیریت نشریات علمی, 11, 4, 1400, 527-534. doi: 10.31661/jbpe.v0i0.2103-1293
Heidari, Marjan, Taghizadeh, Mehdi, Masoumi, Hassan, Valizadeh, Morteza. (1400). 'Liver Segmentation in MRI Images using an Adaptive Water Flow Model', سامانه مدیریت نشریات علمی, 11(4), pp. 527-534. doi: 10.31661/jbpe.v0i0.2103-1293
Heidari, Marjan, Taghizadeh, Mehdi, Masoumi, Hassan, Valizadeh, Morteza. Liver Segmentation in MRI Images using an Adaptive Water Flow Model. سامانه مدیریت نشریات علمی, 1400; 11(4): 527-534. doi: 10.31661/jbpe.v0i0.2103-1293

Liver Segmentation in MRI Images using an Adaptive Water Flow Model

Journal of Biomedical Physics and Engineering
مقاله 13، دوره 11، شماره 4، مهر و آبان 2021، صفحه 527-534    اصل مقاله (1000.7 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2103-1293
نویسندگان
Marjan Heidari1؛ Mehdi Taghizadeh* 2؛ Hassan Masoumi3؛ Morteza Valizadeh4
1PhD Candidate, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
2PhD, Department of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
3PhD, Department of Biomedical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
4PhD, Department of Electrical and Computer Engineering, Urmia University, Urmia, Iran
چکیده
Background: Identification and precise localization of the liver surface and its segments are essential for any surgical treatment. An algorithm of accurate liver segmentation simplifies the treatment planning for different types of liver diseases. Although liver segmentation turns researcher’s attention, it still has some challenging problems in computer-aided diagnosis.
Objective: This study aimed to extract the potential liver regions by an adaptive water flow model and perform the final segmentation by the classification algorithm.
Material and Methods: In this experimental study, an automatic liver segmentation algorithm was introduced. The proposed method designed the image by a transfer function based on the probability distribution function of the liver pixels to enhance the liver area. The enhanced image is then segmented using an adaptive water flow model in which the rainfall process is controlled by the liver location in the training images and the gray levels of pixels. The candidate liver segments are classified by a Multi-Layer Perception (MLP) neural network considering some texture, area, and gray level features.
Results: The proposed algorithm efficiently distinguishes the liver region from its surrounding organs, resulting in perfect liver segmentation over 250 Magnetic Resonance Imaging (MRI) test images. The accuracy of 97% was obtained by quantitative evaluation over test images, which revealed the superiority of the proposed algorithm compared to some evaluated algorithms.
Conclusion: Liver segmentation using an adaptive water flow algorithm and classifying the segmented area in MRI images yields more robust and reliable results in comparison with the classification of pixels.
کلیدواژه‌ها
Image Enhancement؛ MRI Scans؛ Artificial Intelligence؛ Image Processing؛ Computer-Assisted
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