Dalgacık, Contourlet ve Shearlet Dönüşümleri Kullanılarak Çarpanların Alternatif Yön Yöntemi ile Dijital Patoloji Görüntüsü Geriçatılması

Abstract

Dijital patoloji, patoloji bilgilerinin elde edilmesi, çıkarılması ve yorumlanmasının hesaplamalı tekniklerle desteklendiği görüntü tabanlı ortamı ifade eder. Teşhis sürecini kolaylaştırma açısından büyük bir potansiyele sahiptir ancak büyük veri boyutu ve geniş depolama alanlarının gerekliliği zorlayıcıdır. Bu nedenle, bu araştırmada, yeniden yapılandırma için veri miktarını azaltmak amacıyla Sıkıştırılmış Algılama (CS) şeması dijital patoloji görüntüleri ile incelenmiştir. CS, başarılı bir kurtarma için sinyallerin seyrekliğini gerektirir; bu, farklı seyrekleştirici bazların nihai performansı değiştirebileceği anlamına gelir. Dijital patoloji görüntülerini seyrekleştirmek için Dalgacık, Contourlet ve Shearlet Dönüşümleri incelenmiştir, Contourlet Dönüşümünün üstün olduğu görülmüştür. Geriçatma için Alternatif Yön Çarpan Yöntemi (ADMM) sağlam ve hızlı bir dışbükey optimizasyon yöntemi olduğundan seçilmiştir. Dijital patoloji görüntülerinin klasik görüntülere göre daha az seyrek olmasına rağmen CS geriçatması tatmin edicidir, bu da CS'nin dijital patoloji için potansiyelini vurgulamaktadır. Bu çalışma, dijital patoloji ile CS alanında öncü olabilir ve farklı tipte mikroskoplarla veya farklı dalga boylarında CS tabanlı görüntülemeye yönelik daha ileri çalışmaları teşvik edebilir.

Keywords

• Sıkıştırılmış Algılama
• Dijital Patoloji
• Dalgacık Dönüşümü
• Contourlet Dönüşümü
• Shearlet Dönüşümü

Digital Pathology Image Reconstruction with Alternating Direction Method of Multipliers using Wavelet, Contourlet and Shearlet Transforms

Abstract

Digital pathology refers to image-based environment in which acquisition, extraction and interpretation of pathology information is supported by computational techniques. It has a huge potential to facilitate the diagnostic process, however, big data size and necessity of large storage areas are challenging. Therefore, in this research, Compressed Sensing (CS) scheme is studied with digital pathology images in order to reduce the amount of data for reconstruction. CS requires the sparsity of signals for a successful recovery which means that different sparsifying bases can alter the final performance. Wavelet, Contourlet and Shearlet Transforms are investigated to sparsify the digital pathology images, it is seen that Contourlet Transform is superior. Alternating Direction Method of Multipliers (ADMM) is chosen for reconstruction since it is a robust and fast convex optimization method. Despite the fact that digital pathology images are less sparse than classical images, CS reconstruction is satisfactory, which emphasizes the potential of CS for digital pathology. This study can be pioneering in the field of CS with digital pathology so it can encourage further studies of CS based imaging with different type of microscopes or at different wavelengths.

Keywords

• Compressed Sensing
• Digital Pathology
• Wavelet Transform
• Contourlet Transform
• Shearlet Transform

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