File size: 3,890 Bytes
6983ecb c5da400 6983ecb 7b916d6 6983ecb 41e000e a5d94bc 6983ecb 7b916d6 74c5446 7b916d6 74c5446 a5d94bc 7b916d6 6983ecb 8700b06 6983ecb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
import streamlit as st
import os
from PIL import Image
import numpy as np
import cv2
from Utils import *
from huggingface_hub import hf_hub_download,from_pretrained_keras
model = from_pretrained_keras("SerdarHelli/Knee-View-Merchant-Landmark-Detection")
st.header("Knee Merchant Landmark Detection")
st.markdown("***Measurement of Merchant Angles is a fully automated method to measure Patellar Congruence Angle and Tilt Angle on Merchant Knee radiographs, employing CNN landmark localizers*** ")
link='[S.Serdar Helli and Andaç Hamamcı - Yeditepe Medical Imaging Lab. ! ](https://imagingyeditepe.github.io/software.html)'
st.markdown(link,unsafe_allow_html=True)
image_file = st.file_uploader("Upload Images", type=["dcm"])
st.text("Merchant Knee View Dicom Examples ")
examples=["1.3.46.670589.30.1.6.1.149885691756583.1510655758812.1.dcm"
,"1.2.392.200036.9125.9.0.235868094.418384128.208354950.dcm",
"1.2.392.200036.9107.500.304.423.20170526.173028.10423.dcm"]
colx1, colx2, colx3 = st.columns(3)
with colx1:
st.text("Example -1 ")
if st.button('Example 1'):
image_file=examples[0]
with colx2:
st.text("Example -2 ")
if st.button('Example 2'):
image_file=examples[1]
with colx3:
st.text("Example -3 ")
if st.button('Example 3'):
image_file=examples[2]
if image_file is not None:
st.text("Making A Prediction ....")
try:
data,PatientName,PatientID,SOPInstanceUID,StudyDate,InstitutionAddress,PatientAge,PatientSex=read_dicom(image_file,False,True)
except:
data,PatientName,PatientID,SOPInstanceUID,StudyDate,InstitutionAddress,PatientAge,PatientSex=read_dicom(image_file,True,True)
pass
img = np.copy(data)
#Denoise Image
kernel =( np.ones((5,5), dtype=np.float32))
img2=cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel,iterations=2 )
img2=cv2.erode(img2,kernel,iterations =2)
if len(img2.shape)==3:
img2=img2[:,:,0]
#Threshhold 100- 4096
ret,thresh = cv2.threshold(img2,100, 4096, cv2.THRESH_BINARY)
#To Thresh uint8 becasue "findContours" doesnt accept uint16
thresh =((thresh/np.max(thresh))*255).astype('uint8')
a1,b1=thresh.shape
#Find Countours
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
#If There is no countour
if len(contours)==0:
roi= thresh
else:
#Get Areas
c_area=np.zeros([len(contours)])
for i in range(len(contours)):
c_area[i]= cv2.contourArea(contours[i])
#Find Max Countour
cnts=contours[np.argmax(c_area)]
x, y, w, h = cv2.boundingRect(cnts)
#Posibble Square
roi = croping(data, x, y, w, h)
# Absolute Square
roi=modification_cropping(roi)
# Resize to 256x256 with Inter_Nearest
roi=cv2.resize(roi,(256,256),interpolation=cv2.INTER_NEAREST)
pre=predict(roi,model)
heatpoint=points_max_value(pre)
output=put_text_point(roi,heatpoint)
output,PatellerCongruenceAngle,ParalelTiltAngle=draw_angle(output,heatpoint)
data_text = {'PatientID': PatientID, 'PatientName': PatientName,
'Pateller_Congruence_Angle': PatellerCongruenceAngle,
'Paralel_Tilt_Angle':ParalelTiltAngle,
'SOP_Instance_UID':SOPInstanceUID,
"StudyDate" :StudyDate,
"InstitutionName" :InstitutionAddress,
"PatientAge" :PatientAge ,
"PatientSex" :PatientSex,
}
st.text("Original Dicom Image")
st.image(np.uint8((data/np.max(data)*255)),width=450)
st.text("Predicted and Cropped-Resized Image ")
st.image(np.uint8(output),width=450)
st.write(data_text)
|