Publisher

IAESCOREIAESCORE

Journal International Journal of Electrical and Computer Engineering (IJECE)International Journal of Electrical and Computer Engineering (IJECE) Abstract

Edge detection is an important assignment in image processing, as it is used as a primary tool for pattern recognition, image segmentation and scene analysis. An edge detector is a high-pass filter that can be applied for extracting the edge points within an image. Edge detection in the spatial domain is accomplished through convolution with a set of directional derivative masks in this domain. On the other hand, working in the frequency domain has many advantages, starting from introducing an alternative description to the spatial representation and providing more efficient and faster computational schemes with less sensitivity to noise through high filtering, de-noising and compression algorithms. Fourier transforms, wavelet and curvelet transform are among the most widely used frequency-domain edge detection from satellite images. However, the Fourier transform is global and poorly adapted to local singularities. Some of these draw backs are solved by the wavelet transforms especially for singularities detection and computation. In this paper, the relatively new multi-resolution technique, curvelet transform, is assessed and introduced to overcome the wavelet transform limitation in directionality and scaling. In this research paper, the assessment of second generation curvelet transforms as an edge detection tool will be introduced and compared with first generation curvelet transform.

Conclusion

The quantitative performance results of both segmentation and enhancement steps show that the method effectively detects the blood vessels with accuracy of above 94% in less than 1 minute.However, there is a need for a proper Thresholding algorithm to find the small vessels, while avoiding false-edge pixels detection.Also, in retinal images containing severe lesions, the algorithm needs to benefit from a higher level Thresholding method or a more proper scheme.

Future Research

Penelitian lebih lanjut dapat dilakukan untuk mengembangkan algoritma thresholding yang lebih adaptif dan cerdas, yang mampu membedakan antara pembuluh darah kecil yang sebenarnya dan noise atau artefak pada citra retina. Selain itu, studi komparatif yang lebih mendalam dapat dilakukan dengan membandingkan performa curvelet transform generasi kedua dengan teknik segmentasi pembuluh darah retina berbasis deep learning, untuk mengidentifikasi keunggulan dan kelemahan masing-masing metode. Terakhir, penelitian dapat difokuskan pada pengembangan metode pra-pemrosesan citra yang lebih efektif untuk mengurangi dampak lesi parah pada akurasi segmentasi pembuluh darah, misalnya dengan menggunakan teknik peningkatan kontras adaptif atau metode penghilangan noise berbasis pembelajaran mesin.

Metadata

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