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

Esra Şengün Ermeydan; İlyas Çankaya

Makale detayları

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

Yazarlar:

Esra Şengün Ermeydan | İlyas Çankaya

Yayıncı:

Fırat University

Ana Başlıklar:

Image Processing | Biomedical Imaging | Signal Processing

Dergi:

Turkish Journal of Science and Technology Vol.19 Issue 1, pp.169-178

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309699

pISSN:

1308-9080

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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 CSnin 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.

Makale detayları

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

Yazarlar:

Esra Şengün Ermeydan | İlyas Çankaya

Yayıncı:

Fırat University

Ana Başlıklar:

Image Processing | Biomedical Imaging | Signal Processing

Dergi:

Turkish Journal of Science and Technology Vol.19 Issue 1, pp.169-178

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309699

pISSN:

1308-9080

Compressed Sensing

Digital Pathology

Wavelet Transform

Contourlet Transform

Shearlet TransformSıkıştırılmış Algılama

Dijital Patoloji

Dalgacık Dönüşümü

Contourlet Dönüşümü

Shearlet Dönüşümü

Sayı

Makale detayları

Yazarlar: Esra Şengün Ermeydan | İlyas Çankaya

Yayıncı: Fırat University

Ana Başlıklar: Image Processing | Biomedical Imaging | Signal Processing

Dergi: Turkish Journal of Science and Technology Vol.19 Issue 1, pp.169-178

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001309699

pISSN: 1308-9080

Sayı

Yazarlar:

Esra Şengün Ermeydan | İlyas Çankaya

Yayıncı:

Fırat University

Ana Başlıklar:

Image Processing | Biomedical Imaging | Signal Processing

Dergi:

Turkish Journal of Science and Technology Vol.19 Issue 1, pp.169-178

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001309699

pISSN:

1308-9080