Tıbbi görüntüler

Doktorların ilgili ayrıntıları fark etme şansını artırmak için röntgen görüntülerini ön işleyerek bir hastanenin araçlarını iyileştirmeye çalışıyorsun. Kodumuzu, National Institutes of Health Chest X-Ray Dataset içindeki bir akciğer röntgeni üzerinde test edeceksin.

Akciğer röntgeni görüntüsü

Görüntü chest_xray_image olarak yüklendi.

Önce görüntünün histogramını inceleyecek, ardından kontrastı iyileştirmek için standart histogram eşitlemesi uygulayacaksın. Unutma, histogramı Matplotlib'in plt olarak içe aktarılmış hist() fonksiyonuyla elde ediyoruz.

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import the required module
from ____ import ____

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Görüntü İşleme

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Jump into digital image structures and learn to process them! Extract data, transform and analyze images using NumPy and Scikit-image. With just a few lines of code, you will convert RGB images to grayscale, get data from them, obtain histograms containing very useful information, and separate objects from the background!

Exercise 1: Make images come alive with scikit-image Exercise 2: Is this gray or full of color?Exercise 3: RGB to grayscale Exercise 4: NumPy for images Exercise 5: Flipping out Exercise 6: Histograms Exercise 7: Getting started with thresholding Exercise 8: Apply global thresholding Exercise 9: When the background isn't that obvious Exercise 10: Trying other methods Exercise 11: Apply thresholding

You will learn to <strong>detect object shapes</strong> using edge detection filters, <strong>improve medical images</strong> with contrast enhancement <strong>and even enlarge pictures to five times its original size! </strong> You will also apply morphology to make thresholding more accurate when segmenting images and go to the next level of processing images with Python.

Exercise 1: Filtrelemeye hızlı bir başlangıç Exercise 2: Kenar algılama Exercise 3: Gürültüyü azaltmak için bulanıklaştırma Exercise 4: Kontrast iyileştirme Exercise 5: Bu görüntünün kontrastı nedir?Exercise 6: Tıbbi görüntüler

Geçerli Egzersiz

Exercise 7: Hava görüntüsü Exercise 8: Hadi biraz etki ve kontrast katalım Exercise 9: Dönüşümler Exercise 10: Aliasing, döndürme ve yeniden ölçekleme Exercise 11: Görüntüleri büyütme Exercise 12: Oranlı yeniden boyutlandırma Exercise 13: Morfoloji Exercise 14: El yazısı harfler Exercise 15: Eşiklenmiş görüntüyü iyileştirme

So far, you have done some very cool things with your image processing skills! In this chapter, you will apply <strong>image restoration to remove objects, logos, text, or damaged areas</strong> in pictures! You will also learn how to apply noise, use segmentation to speed up processing, and find elements in images by their contours.

Exercise 1: Image restoration Exercise 2: Let's restore a damaged image Exercise 3: Removing logos Exercise 4: Noise Exercise 5: Let's make some noise!Exercise 6: Reducing noise Exercise 7: Reducing noise while preserving edges Exercise 8: Superpixels & segmentation Exercise 9: Number of pixels Exercise 10: Superpixel segmentation Exercise 11: Finding contours Exercise 12: Contouring shapes Exercise 13: Find contours of an image that is not binary Exercise 14: Count the dots in a dice's image

After completing this chapter, you will have a deeper knowledge of image processing as you will be able to <strong>detect edges, corners, and even faces! </strong> You will learn how to detect not just front faces but also face profiles, cat, or dogs. You will apply your skills to more complex <strong>real-world applications.</strong> Learn to master several widely used image processing techniques with very few lines of code!

Exercise 1: Finding the edges with Canny Exercise 2: Edges Exercise 3: Less edgy Exercise 4: Right around the corner Exercise 5: Perspective Exercise 6: Less corners Exercise 7: Face detection Exercise 8: Is someone there?Exercise 9: Multiple faces Exercise 10: Segmentation and face detection Exercise 11: Real-world applications Exercise 12: Privacy protection Exercise 13: Help Sally restore her graduation photo Exercise 14: Amazing work!